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Quality access control of compressed color images using data hiding
ARTICLE IN PRESS ¨ ) 64 (2010) 833–843 Int. J. Electron. Commun. (AEU
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¨) Int. J. Electron. Commun. (AEU journal homepage: www.elsevier.de/aeue
Quality access control of compressed color images using data hiding Amit Phadikar a,1, Santi P. Maity b, a b
Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah 711204, India Department of Information Technology, Bengal Engineering and Science University, Shibpur, P.O. Botanic Garden, 711 103, India
a r t i c l e in fo
abstract
Article history: Received 26 February 2009 Accepted 18 June 2009
This paper proposes a scheme for quality access control of discrete cosine transform (DCT) based compressed color image(s) using data hiding. The goal of access control is achieved by transforming the original image into the luminance, chrominance-blue and chrominance-red (YC b Cr ) color space followed by modulating the coefficients of luminance (Y) and/or chrominance-red (Cr ) channels. The amount of modulation and the information about the specific channels to be modulated are contained in a key K, which is supplied by the owner/service provider. This key and the original image are used to create a content dependent key. The content dependent key is then embedded into the chrominanceblue (Cb ) component as watermark using spread spectrum modulation. This watermark information is used for demodulation that leads to the access control through the restoration of the relative quality of the images. Simulation results show that the proposed scheme provides an effective quality access control for the compressed color image without decreasing the compressibility of the standard JPEG coding scheme. The performance of the proposed method is also compared with the few other methods. & 2009 Elsevier GmbH. All rights reserved.
Keywords: Passive data hiding Watermarking Access control Compressed domain Spread spectrum
1. Introduction The digital information revolution has brought about a profound changes in our society and life. The many advantages of digital information have generated much opportunities for innovation and new challenges. Along with the powerful software, new devices such as digital camera and camcorder, high quality scanners and printers, etc. have offered flexibility to the consumers spread over worldwide to create, manipulate and enjoy multimedia data. Accompanied by them are the availability of Internet and wireless network that offer obliquity channels to deliver and exchange digital information like image and video worldwide. Unfortunately, such data transmission facilities become delicate whenever security and integrity of digital data is concerned, typically for commercial applications or protection of proprietary data. Access control technique may find its usage to provide a kind of security either to deny fully or to allow partial accessing of the digital content [1,2]. The marketing strategies of the manufacturers and the vendors often allow to enjoy a certain low quality data to the non-authorized users in order to attract them for digital content with full quality. In other words, the service provider permits the unauthorized receiver to view sufficiently degraded digital content with an expectation that
Corresponding author. Tel.: + 91 33 2668 4561; fax: +91 33 2668 2916.
E-mail addresses: [email protected] (A. Phadikar), [email protected] (S.P. Maity). 1 Fax: + 91 3326549318. 1434-8411/$ - see front matter & 2009 Elsevier GmbH. All rights reserved. doi:10.1016/j.aeue.2009.09.004
the viewers may be potential customers in future. Thus an efficient access control scheme typically allows all the receivers of the broadcast channel to display a low quality data, for example, image with no or little commercial value [3]. But at the same time, the authorized users can have an access of an image at higher quality levels depending on access rights that usually is determined by the subscription agreements. Such digital contents, in general, are represented in their standard compressed form. This compressed form may be JPEG and more recently like JPEG-2000 for digital images. This requirement in turn demands the development of a quality access control scheme for the compressed data. One typical application of access control may be seen in future generation mobile communication system where billing is expected to be performed based on the fulfillment of degree of quality of services (QoS). The need of access control has received widespread attention and a number of solutions have been proposed in [4–8]. Access control is normally done through modulation of transform coefficients (like DCT, discrete wavelets transform (DWT), etc.) of the image and the video signals using some secret key. Typically two classes of techniques, namely data encryption and data hiding either separately or in combined form are used to meet the goal of access control. We classify them here as non-data hiding and data hiding based access control methods, respectively. 1.1. Non-data hiding based access control Recently data encryption techniques become popular for secured commutation. Some of these concepts are now being
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used for access control. Imaizumi et al. [1] offer a new private-key encryption for JPEG-2000 code streams to meet flexible access control of layers, resolution levels and color components based on all kinds of scalability. Authors pointed out that conventional access control schemes generally use several keys to control image quality. However, their work uses only one master key for both data management and delivery. At the same time, a different key generated by the master key is delivered to the users that permits to access relative qualities of the video signals. Grosbois et al. [2] propose a combined authentication and access control (on image resolutions and qualities) technique of an image in wavelet domain that can be easily integrated in a JPEG-2000 codec. They add the encrypted hash information in the bit stream for checking authentication so that the resulting code streams remain compliant with the standard. On the other hand, the access controls at resolution and in quality are achieved by adding pseudo-random noise in the high frequency subbands and by scrambling of layer, respectively. The cryptographic approach used for access control is usually complex. Moreover, an error in a cipher text may lead to a partially or totally failed decryption [9]. In a broadcast environment, where high volume of data transmission may occur from time to time and no retransmission is allowed, this method becomes a serious problem. To overcome the above problem, Liu et al. [3] propose computationally efficient, secure, selective encryption scheme for JPEG-2000 images without decreasing the compressibility of the standard JPEG-2000 coding scheme. The proposed scheme uses a secret key and a mapping function to generate a private initial table. The table is used to encrypt the selected discrete wavelets transform (DWT) code blocks in the entropy coding stage of JPEG-2000 coding scheme. Won et al. [4] propose a conditional access control scheme. It protects scalable video coding (SVC) bit stream using encryption method developed in the network abstraction layer (NAL). Besides this conditional access control mechanism, key management is also proposed to use the SVC bit stream protected with proposed method. Bertino et al. [5] propose a novel approach to support multilevel access control in video databases. The method combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e. video database indexing units) so that different classes of users can access different video elements (a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e. region of interest)) or even the same video element with different quality levels according to their permissions. Moreover, they also discuss the application of their multilevel video database modeling, representation and indexing for MPEG-7. Wen et al. [6] introduce a new method of performing selective encryption and spatial/frequency shuffling of compressed MPEG-4 video content in the wireless environment. They found that by selectively encrypting and shuffling methods a good trade-off can be made among the various critical fields of the compressed bit stream, complexity, security, bit rate overhead, and functionality.
1.2. Data hiding based access control Digital data hiding, although originally developed for copyright protection, ownership verification and authentication are now being used for assessment of quality of services (QoS) and access control of multimedia data due to commercial and/or security reasons [10–12]. In literature, active data hiding (popularly known as watermarking) is commonly used for former class of applications while the latter purpose is served by passive data hiding methods. Passive data hiding is a technique used for media identification where it is expected that signal distortions caused
due to data hiding can be reverted by the authorized user to enjoy the full quality. Manipulation in the image for controlling its access at different relative qualities to the different categories of users is generally guided by the content of the original image. Pickering et al. [7] suggest a blind data hiding scheme in complex wavelet for access control of video where compliant DVD player denies access to the pirated copy of video. The watermark length is of six bits and is embedded in the high textured areas of the video frames. Chang et al. [8] propose a structure to perform layered access control on scalable media by combining encryption and robust data hiding in uncompressed domain. They pointed out two issues i.e. the key protection and the key synchronization during delivery of the encrypted contents over a broadcast environment. The scheme provides the stronger error-protection of the received key, as the robust watermark can tolerate transmission errors/attacks. The review of the previous works reveals that the multiresolution attribute of wavelets and the use of encryption technique offer access control at different quality of image and video signals. Although new standard of compression like JPEG-2000 has been introduced and wavelet becomes appealing but in reality more than 80% of the image and the video data are still available in DCT compressed form. So the development of efficient quality access control scheme for DCT compressed images is quite demanding and needs attention of the research community. However, algorithms developed and reported in literature, to the best of our knowledge, are also less in number. Recently, Phadikar et al. [13] propose a quality access control of gray scale image in DCT compressed domain. They send the secret key as a metadata at the end of the bit string. However, the key distribution during designing of an access control scheme may introduce three problems: (1) the key requires stronger protection from transmission errors as decryption totally depends on the key; (2) when the keys and the contents are transmitted through a channel that does not guarantee receiving order, the synchronization among the contents and the keys become challenging issue; (3) sending of a secret key as a metadata, at the end of the bit string, may be lost especially if the content undergoes a variety of format changes [14]. To take into account the fact of wide use of DCT compression and the key distribution problem mentioned above, this work embeds the secret key as a watermark in the DCT coefficients of the chrominance blue (Cb ) channel of the color image using spread spectrum (SS) modulation. The key supplied by the owner/service provider contains information about the amount of modulation for the coefficients as well as their belongingness to the particular channels. These operations are collectively called here as passive data hiding in the alternating current (AC) coefficients. The necessary information is encrypted in the form of a secret key, which is embedded as a watermark. The key in the form of watermark is extracted at the receiver side. It is then used at the time of decoding for the encoded data. The simulation results show that only users with complete knowledge of the key can fully recover the best quality of the image(s), whereas all other users can only access to a low quality version of it. The paper is organized as follows: Section 2 describes the proposed image access control scheme while the performance evaluation of the scheme is demonstrated in Section 3. Conclusions are drawn in Section 4 along with the scope of future work.
2. Proposed image access control scheme The proposed image access control scheme consists of two modules, namely image encoding and image decoding. The image encoding module basically performs compression, modulation and symbol encoding, while the image decoding module does the reverse operations i.e. symbol decoding, demodulation and decom-
ARTICLE IN PRESS ¨ ) 64 (2010) 833–843 A. Phadikar, S.P. Maity / Int. J. Electron. Commun. (AEU
pression. The block diagram representations of the image encoding and the image decoding processes in detail are shown in Figs. 1 and 2, respectively. 2.1. Image encoding process The inputs to the encoding process are the color image, the owner key (K), and a set of secret keys for the generation of pseudo-random noise (PRN) matrix (code pattern). The code patterns are used for spreading of watermark bit embedding. The output of the encoding process is the modulated compressed image. The encoding process consists of the following steps. Step 1: Color space transformation: The host color image intended for access control is first transformed from red, green and blue (RGB) into luminance/chrominance color space such as luminance, chrominance-blue and chrominance-red (YC b Cr ) using the following rules: Y ¼ 0:229ðR GÞ þ Gþ 0:114ðB GÞ Cb ¼ 0:564ðB YÞ Cr ¼ 0:713ðR YÞ
ð1Þ
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where the symbols R, G, B, Y, Cb and Cr denote the red, the green, the blue, the luminance, the chrominance-blue and the chrominance-red channels, respectively. This conversion is done as YC b Cr color space allows greater compression for the same image quality or better image quality for the same degree of compression operation. Step 2: Method of key generation for encoding process: To implement the proposed method, a content dependent secret key, which would act as a watermark ‘W’ of length ‘L’, is generated using the following steps. (A) Access control key and its significance: The owner or the service provider of the digital media generates a master key (K) of length ‘L’ bit. This ‘L’ bits data contain the required information about the type of modulation to be performed on each block and also the coefficients of the particular channels to be modulated. Fig. 3 shows a typical such key where the first most significant bits (MSB) i.e. bit ðL 1Þth and ðL 2Þth represent the coefficients of the particular channels to be modulated. The bits in position from ðL 3Þth to ðL n0 Þth are used to represent the percentage of non-zero AC coefficients to be modulated. The rest of the bits are used for sub-key generation. (B) Feature extraction: The chrominance blue (Cb ) channel of the color image is partitioned into non-overlapping blocks of
Fig. 1. Block diagram representation of proposed quality access control (a) encoding process, (b) detail of encoder.
Fig. 2. Block diagram representation of proposed quality access control (a) decoding process, (b) detail of decoder.
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embedded in the chrominance blue (Cb ) channel. The chrominance blue channel is used for embedding, as it is less sensitive to HVS (human visual system). One watermark bit is embedded into each non-overlapping block by modulating the DCT coefficients using spread spectrum (SS) technique. We adopt SS principle for watermark embedding, as it has been proven to be robust and cryptographically secured [15]. The bit is embedded according to the following antipodal signaling scheme. Fig. 3. ‘L’ bit owner defined binary key (K).
( Xie
Table 1 Significance of first four MSB of the key (K). Bit 15th
Bit 14th
Modulation
Bit 13th
Bit 12th
% of modulation (say)
0 0 1 1
0 1 0 1
No Y Cr Y and Cr
0 0 1 1
0 1 0 1
P1% P2% P3% P4%
(case-4) (case-3) (case-2) (case-1)
pixels with size (m0 m0 ). The pixel values are level shifted by subtracting 2m1 value from each pixel value of the blocks, where ‘m’ indicates the number of bits required to represent the gray level of the image. Such an offset makes certain processing, such as numerical overflow, arithmetic coding, context specification, etc. simpler. In particular, this allows the compression more efficient with absolutely no decrease in quality. Discrete cosine transform (DCT) is then performed over each block. A binary vector equal to the length of the sub-key is generated from the image blocks. Each element of this vector is generated from an individual image block according to the following rule. If DC coefficient i.e. the 0th order DCT coefficient of a block is positive, 8 > <1 Hi ¼
> :
else
ð2Þ
0
where the subscript ‘i’ indicates the i th block and Hi indicates the binary element corresponding to the i th block. Each element of vector Hi being constructed from the individual image block, this vector is called here feature vector. We now consider a typical key of 16 bits length where first two MSB corresponds to the coefficients of particular channel to be modulated. The next two MSB indicates percentage of coefficients to be modulated and the rest 12 bits are used to form the content dependent watermark. Table 1 describes the functionality of four MSB for this typical key. This 12 bits binary string is generated from 12 nonoverlapping image blocks of size (128 128) and is selected pseudo-randomly from 16 number of blocks of an image of size (512 512). (C) Sub-key generation: The sub-key (S) is formed by the combination of ‘ L n0 1’ bits of the master key (K) starting from least significant bit (LSB) and the image feature vector (H). The generation rule is guided by the following logical operation as follows: Si ¼ Ki Hi
¼
Xi þ a Pi
if Wi ¼ 1
Xi a Pi
if Wi ¼ 0
where Xi and Xie are the DCT coefficients of i th block of the host and the watermarked images, respectively. The symbol Pi is two dimensional (2D) pseudo-random noise (PRN) matrix (code pattern) of size (m0 m0 ) and is used for i th watermark bit embedding. The symbol ‘a’ is the modulation index or the embedding strength. Then inverse DCT is performed to reconstruct the Cb channel and finally the watermarked Cb channel is formed. Step 4: Down sampling: It has been observed that the chrominance components are less sensitive to human visual system (HVS). This in turn suggests that those components may be down sampled to save space of about 33% or 50% taken by the image. The ratios at which the down sampling can be done on encoder are 4:4:4 (no down sampling), 4:2:2 (reduce by factor of 2 in horizontal direction), and most commonly 4:2:0 (reduce by factor of 2 in horizontal and vertical directions). If the luminance component is not used for data modulation, there is no noticeable loss of image quality [16]. Fig. 4 shows various commonly used sampling ratios in compression. Step 5: Block splitting for JPEG compression: Each channel, after sub-sampling, is partitioned into non-overlapping blocks of pixels with block size (8 8). The (8 8) block size is chosen in order to make the scheme compliant with the JPEG codec. The pixel values of each block are then level shifted by subtracting 2m1 . Discrete cosine transform (DCT) is performed over each block. The resulting coefficients thus obtained are normalized and quantized using standard quantization table used by baseline JPEG. JPEG standard recommends certain quantization tables for the luminance and the chrominance components, based on the studies of human visual property [16,17]. The resultant quantized coefficients are then reordered with the zigzag pattern. Most of the non-zero coefficients are concentrated at the start of the resulting sequence and are followed by runs of zero’s interspersed by a few non-zero coefficients. Step 6: Block based modulation on luminance (Y)/chrominance (Cr ) channel: In this step, data modulation is done based on the access control key which is embedded in the form of watermark in step 3. If the number of non-zero AC coefficients in a (8 8) block is greater than a predefined threshold (T), ‘x’% of the non-zero AC coefficients are selected starting from the end-of-block (EOB). Modulation is then done pseudo-randomly depending on the subkey (S). The modulation is performed according to the following rule. C e ¼ ð1Þ C
ð5Þ
ð3Þ
where ‘i’ satisfies the condition L n0 1 r ir 0. This sub-key, which is a part of access control key, is used to implement the respective modulation for the non-zero AC coefficients of a block. Step 3: Efficient and secret transmission of the secret key: The modified access control key acts as watermark (W) and is
ð4Þ
Fig. 4. 4:2:0 and 4:2:2 sampling.
ARTICLE IN PRESS ¨ ) 64 (2010) 833–843 A. Phadikar, S.P. Maity / Int. J. Electron. Commun. (AEU
where C and C e are the quantized DCT coefficients before and after modulation. Step 7: Efficient representation of bit symbol: Each non-zero coefficient and each run of zero coefficients are replaced by Huffman code. The code is written in a file and they constitute the compressed modulated data.
2.2. Image decoding process The decoding process is just reverse to that of the encoding process. The inputs to the decoder are the modulated compressed Huffman bit sequence and the set of secret key (seed). This is the same key that is used at the time of watermark embedding for the generation of PRN (pseudo-random noise) matrix (code pattern)
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Pi . The output will be of good quality image if the extracted key is correct and vice versa. The steps for decoding process are described as follows. Step 1: Huffman decoding and key (W) extraction: The extraction of the access control key is nothing but the process of watermark decoding, which consists of the following steps. (A) Reconstruction of Cb component: Huffman decoding, reverse zigzag scan, denormalization, inverse DCT and up sampling are performed on the resultant quantized coefficients of the chrominance (Cb ) component in order to reconstruct this channel. (B) Watermark bit extraction from Cb component: Watermark bits are then extracted from ‘L’ number of non-overlapping blocks of same size (m0 m0 ), which is used during embedding. The typical block size considered here is (128 128) and DCT is performed for those blocks. Then a zero-lag correlation is
Fig. 5. Test images. (a) Pepper; (b) Lena; (c) House; (d) Water ; (e) Boat ; (f) Kid; (g) Opera; (h) Paper machine.
Fig. 6. (a) Decompressed test image, (b) decompressed watermarked image, (c) and (d) results of maximum modulation in the luminance (Y) component with ‘ T ¼ 1’ , (c) decoded image without key, (d) decoded image with the true key, (e) and (f) results of maximum modulation in the chrominance (Cr ) component ‘ T ¼ 1’ , (e) decoded image without key, (f) decoded image with the true key, (g) and (h) results of maximum modulation in both luminance (Y) and chrominance (Cr ) component ‘ T ¼ 1’ , (g) decoded image without key, (h) decoded image with the true key.
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Table 2 Results of images without watermarking and quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Lena House Water
0.23 0.30 0.38
34.20:1 26.26:1 20.90:1
30.20 27.46 28.24
0.80 0.85 0.83
Table 3 Results of images after watermarking but without quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Lena House Water
0.23 0.30 0.38
33.75:1 25.86:1 20.82:1
29.76 26.96 28.01
0.79 0.83 0.83
calculated between the DCT coefficients of the embedded block and the particular code pattern (Pi ) which is used at the time of the embedding. This correlation value acts as decision variable mi where
mi ¼ /Xe ; Pi Sð0Þ
ð6Þ
The zero inside the brackets indicates zero-lag. Then watermark ^ i ) is extracted by the following rules: bit (W ^ i ¼ 1 if mi 4 0 ð7Þ W 0 else ^ i ) is treated as access The extracted bit sequence of watermark (W control key which will be used for block based demodulation to reconstruct the better quality image. Step 2: Block based demodulation of luminance/chrominance red (Cr ) channel: Huffman decoding is performed on luminance/ ^ ) chrominance red (Cr ) channel. Depending on the key (W
Fig. 7. Results if only luminance(Y) component is modulated by the key with ‘ T ¼ 4’ , (a) and (b) quality accesses control for case-1 (100% coefficient), (a) decoded image without key, (b) decoded image with the true key, (c) and (d) quality accesses control for case-2 (90% coefficient), (c) decoded image without key, (d) decoded image with the true key, (e) and (f) accesses control for case-3 (80% coefficient), (e) decoded image without key, (f) decoded image with the true key, (g) and (h) accesses control for case-4 (70% coefficient), (g) decoded image without key, (h) decoded image with the true key.
Fig. 8. Results if only chrominance (Cr ) component is modulated by the key with ‘ T ¼ 4’ , (a) and (b) quality accesses control for case-1 (100% coefficient), (a) decoded image without key, (b) decoded image with the true key, (c) and (d) quality accesses control for case-2 (90% coefficient), (c) decoded image without key, (d) decoded image with the true key, (e) and (f) accesses control for case-3 (80% coefficient), (e) decoded image without key, (f) decoded image with the true key, (g) and (h) accesses control for case-4 (70% coefficient), (g) decoded image without key, (h) decoded image with the true key.
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Fig. 9. Results if both luminance (Y) and chrominance (Cr ) component is modulated by the key with ‘ T ¼ 4’ , (a) and (b) quality accesses control for case-1 (100% coefficient), (a) decoded image without key, (b) decoded image with the true key, (c) and (d) quality accesses control for case-2 (90% coefficient), (c) decoded image without key, (d) decoded image with the true key, (e) and (f) accesses control for case-3 (80% coefficient), (e) decoded image without key, (f) decoded image with the true key, (g) and (h) accesses control for case-4 (70% coefficient), (g) decoded image without key, (h) decoded image with the true key.
Table 4 Results of images if only luminance (Y) component is modulated by the key and decoded without and with the true key for T ¼ 4. % of coeff.
Images
Pepper
Lena
Water
House
100 (WOK)
PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSI BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSI PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM
20.34 0.47 0.25 32.00:1 26.34 0.72 23.07 0.55 0.25 32.00:1 26.34 0.72 23.86 0.58 0.25 32.00:1 26.34 0.72 24.30 0.61 0.25 32.00:1 26.34 0.72
21.17 0.50 0.24 33.70:1 29.76 0.80 23.73 0.57 0.24 33.70:1 29.76 0.80 25.06 0.63 0.24 33.70:1 29.76 0.80 26.06 0.66 0.24 33.70:1 29.76 0.80
18.62 0.29 0.38 20.80:1 28.01 0.83 20.52 0.41 0.38 20.80:1 28.01 0.83 21.94 0.49 0.38 20.80:1 28.01 0.83 22.78 0.55 0.38 20.80:1 28.01 0.83
17.99 0.41 0.31 25.80:1 26.96 0.83 20.99 0.54 0.31 25.80:1 26.96 0.83 22.39 0.61 0.31 25.80:1 26.96 0.83 22.79 0.65 0.31 25.80:1 26.96 0.83
100 (WK) 90 (WOK)
90 (WK) 80 (WOK)
80 (WK) Fig. 10. Results of PSNR for different cases tested for large number of images. 70 (WOK)
extracted in step 1, percentage of coefficients (x%) and the coefficients of the particular channels to be demodulated are determined. If the number of non-zero AC coefficients in a block (8 8) is greater than a predefined threshold (T), ‘x’% of the nonzero AC coefficients are selected starting from the end-of-block (EOB). The coefficients are demodulated pseudo-randomly depending on the sub-key (S). The demodulation is performed according to the following rule: C el ¼ ð1Þ C e e
ð8Þ
70 (WK)
WOK: without key, WK: with true key.
Step 4: Inverse color space transformation: The YC b Cr color components are transformed to get RGB image using the following rule: R ¼ Y þ1:371ðCb 128Þ
el
where C and C are the quantized DCT coefficients before and after demodulation process, respectively. Step 3: Inverse transformation of luminance/chrominance (Cr ) channel coefficients: The reverse zigzag scan, denormalization, inverse DCT and up sampling (for Cr only) are performed on the resultant quantized coefficients found in step 2 in order to reconstruct the luminance/chrominance (Cr ) component.
G ¼ Y 0:698ðCr 128Þ 0:336ðCb 128Þ B ¼ Y þ 1:732ðCb 128Þ
ð9Þ
The authorized users thus access relatively good quality images depending on the subscription agreement. The quality of the images is also determined by the faithful decoding of the
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embedded watermark, the amount and the group of coefficients of the particular channel to be modulated, channel used for watermarking, knowledge of the key, etc. Performance of the proposed quality access control is presented now in the next section.
3. Performance evaluation The performance of the proposed access control scheme is tested over large number of benchmark images [18,19]. All of the Table 5 Results of images if only chrominance (Cr ) component is modulated by the key and decoded without and with the true key for T ¼ 4. % of coeff.
Images
Pepper
Lena
Water
House
100 (WOK)
PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM
23.26 0.66 0.25 32.00:1 26.34 0.72 24.68 0.68 0.25 32.00:1 26.34 0.72 24.92 0.68 0.25 32.00:1 26.34 0.72 25.18 0.68 0.25 32.00:1 26.34 0.72
27.97 0.77 0.24 33.70:1 29.76 0.80 28.27 0.78 0.24 33.70:1 29.76 0.80 28.61 0.78 0.24 33.70:1 29.76 0.80 28.61 0.80 0.24 33.70:1 29.76 0.80
26.49 0.81 0.38 20.8:1 28.01 0.84 26.93 0.82 0.38 20.80:1 28.01 0.84 27.11 0.82 0.38 20.80:1 28.01 0.84 27.12 0.82 0.38 20.80:1 28.01 0.84
25.72 0.81 0.31 25.80:1 26.96 0.83 26.10 0.82 0.31 25.80:1 26.96 0.83 26.20 0.82 0.31 25.80:1 26.96 0.83 26.25 0.82 0.31 25.80:1 26.96 0.84
100 (WK) 90 (WOK)
90 (WK) 80 (WOK)
80 (WK) 70 (WOK)
70 (WK)
WOK: without key, WK: with true key.
Table 6 Results of images if both luminance (Y) and chrominance (Cr ) components are modulated by the key and decoded without and with the true key for T ¼ 4.
test images are of size (512 512), 24-bit/pixel-color RGB image and some of them are shown in Fig. 5. The present study uses peak-signal-to-noise-ratio (PSNR) [20] and mean-structuresimilarity-index-measure (MSSIM) (calculated by the average value of Structural SIMilarity (SSIM) Index of the three-color channels) [21] to quantify the distortion measure as well as quality of the images. We have used sampling rate of type 4:2:0 as it is most commonly used in all compression schemes. Fig. 6(a) shows the decompressed ‘Pepper’ image with various quality measures such as (PSNR 26.77 dB, MSSIM 0.73, bit rate 0.24 bits/pixel, compression ratio 32.56:1), obtained after decompression operation. Table 2 lists the bit rate (BR), compression ratio (CR), PSNR and MSSIM value for some images. Fig. 6(b) shows the decompressed ‘Pepper’ image (PSNR 26.34 dB, MSSIM 0.71, BR 0.24 bits/pixel, CR 32.00:1), after watermarking but without applying proposed quality access control mechanism. Table 3 lists the BR, CR, PSNR and MSSIM values for some images under similar situation.
Table 7 Results of images without watermarking and quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Boat Kid Opera Paper machine
0.32 0.19 0.24 0.29
24.85:1 40.46:1 32.21:1 26.97:1
26.01 33.48 32.53 32.29
0.75 0.90 0.89 0.91
Table 8 Results of images with watermarking but without quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Boat Kid Opera Paper machine
0.32 0.20 0.25 0.30
24.69:1 39.95:1 31.79:1 26.77:1
25.75 32.47 31.27 31.16
0.74 0.89 0.87 0.90
Table 9 Results of images when 90% of non-zero AC coefficient in a block is modulated by the key and decoded without and with the true key for T ¼ 4.
% of coeff.
Images
Pepper
Lena
Water
House
Channel
100 (WOK)
PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM
19.38 0.44 0.25 32.00:1 26.34 0.72 22.22 0.53 0.25 32.00:1 26.34 0.72 23.02 0.56 0.25 32.00:1 26.34 0.72 23.54 0.58 0.25 32.00:1 26.34 0.72
20.89 0.48 0.24 33.70:1 29.76 0.80 23.32 0.56 0.24 33.70:1 29.76 0.80 24.64 0.61 0.24 33.70:1 29.76 0.80 25.54 0.64 0.24 33.70:1 29.76 0.80
18.40 0.28 0.38 20.80:1 28.01 0.83 20.30 0.40 0.38 20.80:1 28.01 0.83 21.69 0.49 0.38 20.80:1 28.01 0.83 22.48 0.53 0.38 20.80:1 28.01 0.83
17.82 0.40 0.31 25.80:1 26.96 0.83 20.76 0.53 0.31 25.80:1 26.96 0.83 22.10 0.60 0.31 25.80:1 26.96 0.83 22.50 0.64 0.31 25.80:1 26.96 0.83
Y (WOK) PSNR (dB) MSSIM BR CR Y (WK) PSNR (dB) MSSIM Cr (WOK) PSNR (dB) MSSIM BR CR Cr (WK) PSNR (dB) MSSIM Y and Cb (WOK) PSNR (dB) MSSIM BR CR Y and Cb (WK) PSNR (dB) MSSIM
100 (WK) 90 (WOK)
90 (WK) 80 (WOK)
80 (WK) 70 (WOK)
70 (WK)
WOK: without key, WK: with true key.
Boat
Kid
Opera
Paper m/c
20.70 0.49 0.32 24.60:1
25.671 0.71 0.20 39.90:1
23.86 0.58 0.25 31.70:1
22.16 0.60 0.30 26.70:1
25.75 0.75
32.47 0.90
31.27 0.88
31.16 0.91
24.50 0.72 0.32 24.60:1
31.49 0.88 0.20 39.90:1
30.42 0.87 0.25 31.70:1
30.13 0.90 0.30 26.70:1
25.75 0.75
32.47 0.89
31.27 0.87
31.16 0.91
20.30 0.48 0.32 24.60:1
25.44 0.71 0.20 39.90:1
23.68 0.58 0.25 31.70:1
22.01 0.59 0.30 26.70:1
25.75 0.75
32.47 0.89
31.27 0.87
31.16 0.91
WOK: without key, WK: with true key.
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Fig. 11. (a) Decompressed test image, (b) decompressed watermarked image, (c) and (d) results of modulation in only luminance (Y) component for case-2 (90% coefficient) with ‘ T ¼ 4’ , (c) decoded image without key, (d) decoded image with the true key, (e) and (f) results of modulation in only chrominance (Cr ) component for case-2 (90% coefficient) with ‘ T ¼ 4’ , (e) decoded image without key, (f) decoded image with the true key, (g) and (h) results of modulation in both luminance (Y) and chrominance (Cr ) component for case-2 (90% coefficient) with ‘ T ¼ 4’ , (g) decoded image without key, (h) decoded image with the true key.
Table 10 Comparison of results: coding rate, KCR, PSNR (dB) and MSSIM. Coding rate (bits/pixel)
Chang [8] Zaidee [22] Proposed
8 1.24 0.24
KCR (%)
BER ¼ 104
BER ¼ 104
BER ¼ 105
BER ¼ 106
PSNR (dB)
MSSIM
76 99 100
99 99 100
100 100 100
35.74 28.84 35.17
0.962 0.910 0.961
It is clear from the results shown in Tables 2 and 3 that the watermarking mechanism, on an average, decreases about 1.12% CR leading to its little impact on data rate. Fig. 6(c, e, g) show the modulated ‘Pepper’ image under T ¼ 1 i.e. when all non-zero AC coefficients of the various channels are modified. Fig. 6(d, f, h) show the decoded image with the true access control key. The distortion is large as most of the energy of a block is concentrated near the direct current (DC) coefficient and are modulated when we take T ¼ 1. We also conduct the similar types of tests by setting the parameter value T ¼ 4. Figs. 7–9 show the results of ‘Pepper’ image if only Y, only Cr and both Y and Cr channels are modulated by the key and are decoded with and without the true key, respectively. In this study, the amount of non-zero AC coefficients to be modulated are of four categories i.e. case-1: 100%, case-2: 90%, case-3: 80%, case-4: 70%. It is seen from Figs. 7–9 that in all test cases decoded ‘Pepper’ images with the true key are of ultimate quality. The decoded images without the proper key produces a lower level of quality. In other words, the images of Figs. 7(a, c, e, g)–9(a, c, e, g) will be available to all users but the images of Figs. 7(b, d, f, h)–9(b, d, f, h) will only be available to the authorized users who have the subscription agreement. Fig. 10 shows the variation of PSNR graphically for the modulation of different channels. The modulation is done for different percentage of coefficients and images are decoded without and with the true key. The numerical values shown in the graphical representation are obtained by taking average for the PSNR values obtained from the test done over large number of
Table 11 Robustness (in term of BER) against various filtering operations. Filter type
Median Wiener Mean Highpass
Mask size 33
55
77
99
11 11
0 0 0 0
0 0 0 0
0.12 0 0.12 0
0.25 0 0.31 0
0.31 0 0.43 0
images. Tables 4–6 list the BR, CR, PSNR and MSSIM values for various images like Pepper, Lena, Water and House. It is clear from the results of Table 5 that modulation of only Cr component is not sufficient to implement efficient access control, as that amount of modulation does not distort signal very much. It is clear from the results shown in Tables 4–6 that for all types of experimentations, modulation process is reverted completely and the full quality of the image is achieved without increasing any bit rate. After studying the results of Tables 4–6, we can conclude that modulation of non-zero AC coefficient by an amount of 90% is enough for access control, as that amount of distortion is sufficient for quality access control of image. By setting this parameter (i.e. percentage of non-zero AC coefficient to be modulated in a block is of 90% and T ¼ 4), we test our scheme for other test images. The results obtained are shown in Tables 7–9. Fig. 11 shows the results of the test image ‘Boat sailing’.
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Table 12 Experimental results with StirMark 4.0. Strength
BER
MEDIAN_5 MEDIAN_7 MEDIAN_9 ROTSCALE_0.25 ROTCROP_0.25 SS_1 SS_2 SS_2 CROPPING_20 CROPPING_50 LATESTRNDDIST_1 LATESTRNDDIST_0.33 REMOV_LINES_50 REMOV_LINES_70 NOISE_60 AFFINE_4 AFFINE_6 CONV_1 CONV_2
0 0.06 0.25 0.25 0.12 0 0 0 0.27 0.27 0.43 0.33 0 0 0 0.25 0.12 0 0
Robustness performance of the proposed scheme against various signal processing operations, such as format changes or transmission over noisy channels is examined. Experimental results show that our scheme is robust to such operations, as one bit of watermark is embedded by spreading over a block of size 128 128 using SS modulation. The robustness of the proposed scheme is also tested against the format change that leads to the change of the color space. The watermark is embedded in the YC b Cr color space and is detected efficiently after transforming it from YC b Cr to RGB and then from RGB to YC b Cr . It ensures that the scheme is robust to color space conversion. The robustness of the scheme over noisy channels is also tested and the result is compared with the results obtained in [8,22] in terms of coding rate, KCR (key correction ratio), PSNR, MSSIM, BER (bit error rate). The word BER here implies the error in the received compressed signal when transmitted through the noisy channel or manipulated by some user. The BER is simulated in this experimentation by randomly manipulating the compressed data. This affects PSNR and MSSIM values of the received image with respect to the compressed version. The term KCR may be defined as the percentage of match between the true key and the extracted key. It is observed from the results shown in Table 10 that the proposed scheme can extract the key correctly (high value of KCR) even with the presence of various bit error. The scheme also decodes the image effectively without increasing overhead than the existing schemes [8,22]. Simulation results show that our method can decode the key with greater reliability at BER of 104 and offers better PSNR and MSSIM values at low coding rate. It is also to be pointed out that PSNR for Chang [8] method is little better compared to our method but the coding rate of the former is much higher than the latter. This in other words show that high PSNR in [8] is possible when there is no compression while our method allows significant compression (low bit rate) even at reasonable quality. Similarly, we also compare the result of the proposed access control method with the work reported in [22]. Results shown in Table 10 highlights the superiority of our method. Robustness performance of the proposed method is also studied against the standard signal processing operations like median filtering, Wiener filtering, mean filtering, highpass filtering, scale down by factor 0.5, dynamic range change (50–200), histogram equalization, etc. The results obtained for filtering operations are shown in Table 11. It is seen that the BER values for
the extracted watermarks due to all standard signal processing operations are quite low even for the mask size (11 11). This is quite natural as the proposed scheme uses SS modulation for data hiding and one bit of watermark is embedded in a block of size 128 128. We also test performance of the proposed scheme against benchmark StirMark 4.0 [23–25]. Table 12 lists the experimental results using StirMark 4.0. The execution time of the proposed scheme is studied and it is seen that the scheme takes on an average 63.05 s (image encoding: 41.71 s; image decoding: 21.34 s) for both image encoding and decoding process. The simulation is conducted on Pentium IV, 2.80 GHz processor, with 504 MB RAM using MATLAB 7 version.
4. Conclusions and scope of future works This paper proposes an efficient quality access control scheme of color images in compressed domain. The users with the complete knowledge of the key can recover the full quality of the image, whereas the other users can only access to a low quality version of it. The secret key that is used for modulation is embedded in the cover image as a watermark using spread spectrum (SS) modulation. Experimental results show that this lowers the risk of losing the information of the secret key during format conversion or due to error in transmission. Moreover, the secret key is totally synchronized to the content. Future work may be explored for the extension of the proposed access control scheme on video and sound data. References [1] Imaizumi S, Watanabe O, Fujiyoshi M, Kiya H. Generalized hierarchical encryption of JPEG 2000 code streams for access control. In: Proceeding of IEEE international conference on image processing, 2005. p. 1094–7. [2] Grosbois R, Gerbelot P, Ebrahimi T. Authentication and access control in the jpeg 2000 compressed domain. In: Proceeding of SPIE 46th annual meeting, applications of digital image processing, 2001. p. 95–104. [3] Liu JL. Efficient selective encryption for JPEG 2000 images using private initial table. Pattern Recognition 2006;39:509–1517. [4] Won YG, Bae TM, Ro YM. Scalable protection and access control in full scalable video coding. In: Lecture Notes in Computer Science, vol. 4283. Berlin: Springer; 2006. p. 407–21. [5] Bertino E, Fan J, Ferrari E, Hacid MS, Elmagarmid AK, Zhu X. A hierarchical access control model for video database systems. ACM Transactions on Information Systems 2003;21:155–91. [6] Wen JG, Severa M, Zeng W, Luttrell MH, Jin W. A format-compliant configurable encryption framework for access control of video. IEEE Transactions on Circuits and Systems for Video Technology 2002;12:545–57. [7] Pickering M, Coria LE, Nasiopoulos P. A novel blind video watermarking scheme for access control using complex wavelets. In: Proceeding of IEEE international conference on consumer electronics, 2007. p. 1–2. [8] Chang FC, Huang HC, Hang HM. Layered access control schemes on watermarked scalable media. Journal of VLSI Signal Processing 2007;49:443–55. [9] Xu X, Dexter S, Eskicioglu AM. A hybrid scheme for encryption and watermarking. In: Proceeding of IS & T/SPIE symposium electronic imaging, 2004. p. 723–34. [10] Petitcolas FAP. Digital watermarking. Berlin: Springer; 2003. [11] Maity SP, Kundu MK, Maity S. Dual purpose FWT domain spread spectrum image watermarking in real-time. International Journal of Computer & Electrical Engineering 2009;35(2):415–33 [special issue on Real-time security and copyright protection of multimedia]. [12] Campisi P, Carli M, Giunta G, Neri A. Blind quality assessment system for multimedia communications using tracing watermarking. IEEE Transaction on Signal Processing 2003;51:996–1002. [13] Phadikar A, Kundu MK, Maity SP. Quality access control of a compressed gray scale image. In: Proceeding of national conference on computer vision, pattern recognition. Image processing and graphics (NCVPRIPG 08), 2008. p. 13–19. [14] Wee SJ, Apostolopoulos JG. Secure scalable streaming enabling transcoding without decryption. In: Proceeding of IEEE international conference on image processing, 2001. p. 437–40. [15] Maity SP, Kundu MK, Das TS. Robust SS watermarking with improved capacity. Pattern Recognition Letters 2007;28:350–6. [16] Solomon D. Data compression: the complete reference. London: Springer; 2007.
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[17] Phadikar A, Maity SP. ROl based quality access control of compressed color image using DWT via lifting. Electronic Letter on Computer Vision and Image Analysis 2009;8:51–67. [18] /http://www.cl.cam.ac.uk/fapp2/watermarkingS. [19] /http://www.petitcolas.net/fabien/watermarking/image_database/index. htmlS. [20] Gonzalez RC, Woods RE. Digital image processing using Matlab. Upper Saddle River, NJ: Prentice-Hall; 2005. [21] Wang ZA, Bovik C, Sheikh HR, Simoncelli EP. Image quality assessment: from error measurement to structural similarity. IEEE Transactions on Image Processing 2004;13:1–14. [22] Zaidee AA, Fazdliana S, Adznan BJ. Content access control for JPEG images using CRND zigzag scanning and QBP. In: Proceedings of the sixth IEEE international conference on computer and information technology (CIT’06), 2006. p. 146. [23] Petitcolas FAP. Watermarking schemes evaluation. IEEE Signal Processing 2000;17:58–64. [24] Petitcolas FAP, Anderson RJ, Kuhn MG. Attacks on copyright marking systems. In: Proceeding of second international workshop on information hiding. Lecture Notes in Computer Science, vol. 1525. Berlin: Springer; 1998. p. 219– 39. [25] /http://www.petitcolas.net/fabien/watermarking/stirmark/S.
Amit Phadikar received his B.E. in Computer Science and Engineering in 2002 from Vidyasagar University, W.B., India and M.Tech degree in Information Technology (Artificial Intelligence) in 2005 from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal, M.P., India. Since 2005 he is working as Lecturer at the Department of Information Technology, MCKV Institute of Engineering, Liluha, Howrah, W.B., India. His research areas include digital image watermarking, digital signal processing, content based image retrieval. Currently he is working towards his Ph.D. degree in Engineering (Information Technology) from Bengal Engineering and Science University, Shibpur, India.
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Santi P. Maity received his B.E. in Electronics and Communication Engineering and M.Tech degree in Microwaves, both from the University of Burdwan, India, in the year 1993 and 1997, respectively. He received his Ph.D. degree in Engineering (Computer Science and Technology) from Bengal Engineering and Science University, Shibpur, India, in the year 2008. During January 2009 to July 2009 he did his posdoctoral work concerning watermarking in lured applications in the ‘Laboratoire des Signaux et Systems (CNRS-Supelec-Universite Paris-Sud 11)’ in France. He is at present working as Assistant Professor at the Department of Information Technology, Bengal Engineering and Science University, Shibpur and acted as Head of the Department from September 2008 to January 2009. He also worked as Lecturer in Electronics and Telecommunication Engineering Department of the same university from 2000 to 2006. Prior to that, he worked as Lecturer in Electronics and Telecommunication Engineering Department of K.G. Engineering Institute, Bishnupur, Bankura, India and Haldia Institute of Technology, Haldia, India, from 1997 to 2000. His research areas include digital image watermarking, multiuser detection in CDMA, digital signal processing, digital wireless communication, VLSI watermarking. He delivers several lectures and invited talk on short term course/seminar/workshop/ conference and acts as committee member for national and international conferences like IMSA 2008, IMSA2009 and IMSA 2010. He is an Associate Member of Institute of Engineer (India) and a Member of Institute of Electronics and Telecommunication Engineers (IETE), India. He is principal investigator of a project ‘High Power and Spectral Efficiency Multiuser System for Broadband Wireless Communication’ funded by Department of Information Technology, Ministry of Communication and Information Technology, Government of India. He has contributed about 70 research papers in well-known and prestigious archival journals, international refereed conferences and as chapters in edited volumes.
Contents lists available at ScienceDirect
¨) Int. J. Electron. Commun. (AEU journal homepage: www.elsevier.de/aeue
Quality access control of compressed color images using data hiding Amit Phadikar a,1, Santi P. Maity b, a b
Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah 711204, India Department of Information Technology, Bengal Engineering and Science University, Shibpur, P.O. Botanic Garden, 711 103, India
a r t i c l e in fo
abstract
Article history: Received 26 February 2009 Accepted 18 June 2009
This paper proposes a scheme for quality access control of discrete cosine transform (DCT) based compressed color image(s) using data hiding. The goal of access control is achieved by transforming the original image into the luminance, chrominance-blue and chrominance-red (YC b Cr ) color space followed by modulating the coefficients of luminance (Y) and/or chrominance-red (Cr ) channels. The amount of modulation and the information about the specific channels to be modulated are contained in a key K, which is supplied by the owner/service provider. This key and the original image are used to create a content dependent key. The content dependent key is then embedded into the chrominanceblue (Cb ) component as watermark using spread spectrum modulation. This watermark information is used for demodulation that leads to the access control through the restoration of the relative quality of the images. Simulation results show that the proposed scheme provides an effective quality access control for the compressed color image without decreasing the compressibility of the standard JPEG coding scheme. The performance of the proposed method is also compared with the few other methods. & 2009 Elsevier GmbH. All rights reserved.
Keywords: Passive data hiding Watermarking Access control Compressed domain Spread spectrum
1. Introduction The digital information revolution has brought about a profound changes in our society and life. The many advantages of digital information have generated much opportunities for innovation and new challenges. Along with the powerful software, new devices such as digital camera and camcorder, high quality scanners and printers, etc. have offered flexibility to the consumers spread over worldwide to create, manipulate and enjoy multimedia data. Accompanied by them are the availability of Internet and wireless network that offer obliquity channels to deliver and exchange digital information like image and video worldwide. Unfortunately, such data transmission facilities become delicate whenever security and integrity of digital data is concerned, typically for commercial applications or protection of proprietary data. Access control technique may find its usage to provide a kind of security either to deny fully or to allow partial accessing of the digital content [1,2]. The marketing strategies of the manufacturers and the vendors often allow to enjoy a certain low quality data to the non-authorized users in order to attract them for digital content with full quality. In other words, the service provider permits the unauthorized receiver to view sufficiently degraded digital content with an expectation that
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E-mail addresses: [email protected] (A. Phadikar), [email protected] (S.P. Maity). 1 Fax: + 91 3326549318. 1434-8411/$ - see front matter & 2009 Elsevier GmbH. All rights reserved. doi:10.1016/j.aeue.2009.09.004
the viewers may be potential customers in future. Thus an efficient access control scheme typically allows all the receivers of the broadcast channel to display a low quality data, for example, image with no or little commercial value [3]. But at the same time, the authorized users can have an access of an image at higher quality levels depending on access rights that usually is determined by the subscription agreements. Such digital contents, in general, are represented in their standard compressed form. This compressed form may be JPEG and more recently like JPEG-2000 for digital images. This requirement in turn demands the development of a quality access control scheme for the compressed data. One typical application of access control may be seen in future generation mobile communication system where billing is expected to be performed based on the fulfillment of degree of quality of services (QoS). The need of access control has received widespread attention and a number of solutions have been proposed in [4–8]. Access control is normally done through modulation of transform coefficients (like DCT, discrete wavelets transform (DWT), etc.) of the image and the video signals using some secret key. Typically two classes of techniques, namely data encryption and data hiding either separately or in combined form are used to meet the goal of access control. We classify them here as non-data hiding and data hiding based access control methods, respectively. 1.1. Non-data hiding based access control Recently data encryption techniques become popular for secured commutation. Some of these concepts are now being
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used for access control. Imaizumi et al. [1] offer a new private-key encryption for JPEG-2000 code streams to meet flexible access control of layers, resolution levels and color components based on all kinds of scalability. Authors pointed out that conventional access control schemes generally use several keys to control image quality. However, their work uses only one master key for both data management and delivery. At the same time, a different key generated by the master key is delivered to the users that permits to access relative qualities of the video signals. Grosbois et al. [2] propose a combined authentication and access control (on image resolutions and qualities) technique of an image in wavelet domain that can be easily integrated in a JPEG-2000 codec. They add the encrypted hash information in the bit stream for checking authentication so that the resulting code streams remain compliant with the standard. On the other hand, the access controls at resolution and in quality are achieved by adding pseudo-random noise in the high frequency subbands and by scrambling of layer, respectively. The cryptographic approach used for access control is usually complex. Moreover, an error in a cipher text may lead to a partially or totally failed decryption [9]. In a broadcast environment, where high volume of data transmission may occur from time to time and no retransmission is allowed, this method becomes a serious problem. To overcome the above problem, Liu et al. [3] propose computationally efficient, secure, selective encryption scheme for JPEG-2000 images without decreasing the compressibility of the standard JPEG-2000 coding scheme. The proposed scheme uses a secret key and a mapping function to generate a private initial table. The table is used to encrypt the selected discrete wavelets transform (DWT) code blocks in the entropy coding stage of JPEG-2000 coding scheme. Won et al. [4] propose a conditional access control scheme. It protects scalable video coding (SVC) bit stream using encryption method developed in the network abstraction layer (NAL). Besides this conditional access control mechanism, key management is also proposed to use the SVC bit stream protected with proposed method. Bertino et al. [5] propose a novel approach to support multilevel access control in video databases. The method combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e. video database indexing units) so that different classes of users can access different video elements (a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e. region of interest)) or even the same video element with different quality levels according to their permissions. Moreover, they also discuss the application of their multilevel video database modeling, representation and indexing for MPEG-7. Wen et al. [6] introduce a new method of performing selective encryption and spatial/frequency shuffling of compressed MPEG-4 video content in the wireless environment. They found that by selectively encrypting and shuffling methods a good trade-off can be made among the various critical fields of the compressed bit stream, complexity, security, bit rate overhead, and functionality.
1.2. Data hiding based access control Digital data hiding, although originally developed for copyright protection, ownership verification and authentication are now being used for assessment of quality of services (QoS) and access control of multimedia data due to commercial and/or security reasons [10–12]. In literature, active data hiding (popularly known as watermarking) is commonly used for former class of applications while the latter purpose is served by passive data hiding methods. Passive data hiding is a technique used for media identification where it is expected that signal distortions caused
due to data hiding can be reverted by the authorized user to enjoy the full quality. Manipulation in the image for controlling its access at different relative qualities to the different categories of users is generally guided by the content of the original image. Pickering et al. [7] suggest a blind data hiding scheme in complex wavelet for access control of video where compliant DVD player denies access to the pirated copy of video. The watermark length is of six bits and is embedded in the high textured areas of the video frames. Chang et al. [8] propose a structure to perform layered access control on scalable media by combining encryption and robust data hiding in uncompressed domain. They pointed out two issues i.e. the key protection and the key synchronization during delivery of the encrypted contents over a broadcast environment. The scheme provides the stronger error-protection of the received key, as the robust watermark can tolerate transmission errors/attacks. The review of the previous works reveals that the multiresolution attribute of wavelets and the use of encryption technique offer access control at different quality of image and video signals. Although new standard of compression like JPEG-2000 has been introduced and wavelet becomes appealing but in reality more than 80% of the image and the video data are still available in DCT compressed form. So the development of efficient quality access control scheme for DCT compressed images is quite demanding and needs attention of the research community. However, algorithms developed and reported in literature, to the best of our knowledge, are also less in number. Recently, Phadikar et al. [13] propose a quality access control of gray scale image in DCT compressed domain. They send the secret key as a metadata at the end of the bit string. However, the key distribution during designing of an access control scheme may introduce three problems: (1) the key requires stronger protection from transmission errors as decryption totally depends on the key; (2) when the keys and the contents are transmitted through a channel that does not guarantee receiving order, the synchronization among the contents and the keys become challenging issue; (3) sending of a secret key as a metadata, at the end of the bit string, may be lost especially if the content undergoes a variety of format changes [14]. To take into account the fact of wide use of DCT compression and the key distribution problem mentioned above, this work embeds the secret key as a watermark in the DCT coefficients of the chrominance blue (Cb ) channel of the color image using spread spectrum (SS) modulation. The key supplied by the owner/service provider contains information about the amount of modulation for the coefficients as well as their belongingness to the particular channels. These operations are collectively called here as passive data hiding in the alternating current (AC) coefficients. The necessary information is encrypted in the form of a secret key, which is embedded as a watermark. The key in the form of watermark is extracted at the receiver side. It is then used at the time of decoding for the encoded data. The simulation results show that only users with complete knowledge of the key can fully recover the best quality of the image(s), whereas all other users can only access to a low quality version of it. The paper is organized as follows: Section 2 describes the proposed image access control scheme while the performance evaluation of the scheme is demonstrated in Section 3. Conclusions are drawn in Section 4 along with the scope of future work.
2. Proposed image access control scheme The proposed image access control scheme consists of two modules, namely image encoding and image decoding. The image encoding module basically performs compression, modulation and symbol encoding, while the image decoding module does the reverse operations i.e. symbol decoding, demodulation and decom-
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pression. The block diagram representations of the image encoding and the image decoding processes in detail are shown in Figs. 1 and 2, respectively. 2.1. Image encoding process The inputs to the encoding process are the color image, the owner key (K), and a set of secret keys for the generation of pseudo-random noise (PRN) matrix (code pattern). The code patterns are used for spreading of watermark bit embedding. The output of the encoding process is the modulated compressed image. The encoding process consists of the following steps. Step 1: Color space transformation: The host color image intended for access control is first transformed from red, green and blue (RGB) into luminance/chrominance color space such as luminance, chrominance-blue and chrominance-red (YC b Cr ) using the following rules: Y ¼ 0:229ðR GÞ þ Gþ 0:114ðB GÞ Cb ¼ 0:564ðB YÞ Cr ¼ 0:713ðR YÞ
ð1Þ
835
where the symbols R, G, B, Y, Cb and Cr denote the red, the green, the blue, the luminance, the chrominance-blue and the chrominance-red channels, respectively. This conversion is done as YC b Cr color space allows greater compression for the same image quality or better image quality for the same degree of compression operation. Step 2: Method of key generation for encoding process: To implement the proposed method, a content dependent secret key, which would act as a watermark ‘W’ of length ‘L’, is generated using the following steps. (A) Access control key and its significance: The owner or the service provider of the digital media generates a master key (K) of length ‘L’ bit. This ‘L’ bits data contain the required information about the type of modulation to be performed on each block and also the coefficients of the particular channels to be modulated. Fig. 3 shows a typical such key where the first most significant bits (MSB) i.e. bit ðL 1Þth and ðL 2Þth represent the coefficients of the particular channels to be modulated. The bits in position from ðL 3Þth to ðL n0 Þth are used to represent the percentage of non-zero AC coefficients to be modulated. The rest of the bits are used for sub-key generation. (B) Feature extraction: The chrominance blue (Cb ) channel of the color image is partitioned into non-overlapping blocks of
Fig. 1. Block diagram representation of proposed quality access control (a) encoding process, (b) detail of encoder.
Fig. 2. Block diagram representation of proposed quality access control (a) decoding process, (b) detail of decoder.
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embedded in the chrominance blue (Cb ) channel. The chrominance blue channel is used for embedding, as it is less sensitive to HVS (human visual system). One watermark bit is embedded into each non-overlapping block by modulating the DCT coefficients using spread spectrum (SS) technique. We adopt SS principle for watermark embedding, as it has been proven to be robust and cryptographically secured [15]. The bit is embedded according to the following antipodal signaling scheme. Fig. 3. ‘L’ bit owner defined binary key (K).
( Xie
Table 1 Significance of first four MSB of the key (K). Bit 15th
Bit 14th
Modulation
Bit 13th
Bit 12th
% of modulation (say)
0 0 1 1
0 1 0 1
No Y Cr Y and Cr
0 0 1 1
0 1 0 1
P1% P2% P3% P4%
(case-4) (case-3) (case-2) (case-1)
pixels with size (m0 m0 ). The pixel values are level shifted by subtracting 2m1 value from each pixel value of the blocks, where ‘m’ indicates the number of bits required to represent the gray level of the image. Such an offset makes certain processing, such as numerical overflow, arithmetic coding, context specification, etc. simpler. In particular, this allows the compression more efficient with absolutely no decrease in quality. Discrete cosine transform (DCT) is then performed over each block. A binary vector equal to the length of the sub-key is generated from the image blocks. Each element of this vector is generated from an individual image block according to the following rule. If DC coefficient i.e. the 0th order DCT coefficient of a block is positive, 8 > <1 Hi ¼
> :
else
ð2Þ
0
where the subscript ‘i’ indicates the i th block and Hi indicates the binary element corresponding to the i th block. Each element of vector Hi being constructed from the individual image block, this vector is called here feature vector. We now consider a typical key of 16 bits length where first two MSB corresponds to the coefficients of particular channel to be modulated. The next two MSB indicates percentage of coefficients to be modulated and the rest 12 bits are used to form the content dependent watermark. Table 1 describes the functionality of four MSB for this typical key. This 12 bits binary string is generated from 12 nonoverlapping image blocks of size (128 128) and is selected pseudo-randomly from 16 number of blocks of an image of size (512 512). (C) Sub-key generation: The sub-key (S) is formed by the combination of ‘ L n0 1’ bits of the master key (K) starting from least significant bit (LSB) and the image feature vector (H). The generation rule is guided by the following logical operation as follows: Si ¼ Ki Hi
¼
Xi þ a Pi
if Wi ¼ 1
Xi a Pi
if Wi ¼ 0
where Xi and Xie are the DCT coefficients of i th block of the host and the watermarked images, respectively. The symbol Pi is two dimensional (2D) pseudo-random noise (PRN) matrix (code pattern) of size (m0 m0 ) and is used for i th watermark bit embedding. The symbol ‘a’ is the modulation index or the embedding strength. Then inverse DCT is performed to reconstruct the Cb channel and finally the watermarked Cb channel is formed. Step 4: Down sampling: It has been observed that the chrominance components are less sensitive to human visual system (HVS). This in turn suggests that those components may be down sampled to save space of about 33% or 50% taken by the image. The ratios at which the down sampling can be done on encoder are 4:4:4 (no down sampling), 4:2:2 (reduce by factor of 2 in horizontal direction), and most commonly 4:2:0 (reduce by factor of 2 in horizontal and vertical directions). If the luminance component is not used for data modulation, there is no noticeable loss of image quality [16]. Fig. 4 shows various commonly used sampling ratios in compression. Step 5: Block splitting for JPEG compression: Each channel, after sub-sampling, is partitioned into non-overlapping blocks of pixels with block size (8 8). The (8 8) block size is chosen in order to make the scheme compliant with the JPEG codec. The pixel values of each block are then level shifted by subtracting 2m1 . Discrete cosine transform (DCT) is performed over each block. The resulting coefficients thus obtained are normalized and quantized using standard quantization table used by baseline JPEG. JPEG standard recommends certain quantization tables for the luminance and the chrominance components, based on the studies of human visual property [16,17]. The resultant quantized coefficients are then reordered with the zigzag pattern. Most of the non-zero coefficients are concentrated at the start of the resulting sequence and are followed by runs of zero’s interspersed by a few non-zero coefficients. Step 6: Block based modulation on luminance (Y)/chrominance (Cr ) channel: In this step, data modulation is done based on the access control key which is embedded in the form of watermark in step 3. If the number of non-zero AC coefficients in a (8 8) block is greater than a predefined threshold (T), ‘x’% of the non-zero AC coefficients are selected starting from the end-of-block (EOB). Modulation is then done pseudo-randomly depending on the subkey (S). The modulation is performed according to the following rule. C e ¼ ð1Þ C
ð5Þ
ð3Þ
where ‘i’ satisfies the condition L n0 1 r ir 0. This sub-key, which is a part of access control key, is used to implement the respective modulation for the non-zero AC coefficients of a block. Step 3: Efficient and secret transmission of the secret key: The modified access control key acts as watermark (W) and is
ð4Þ
Fig. 4. 4:2:0 and 4:2:2 sampling.
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where C and C e are the quantized DCT coefficients before and after modulation. Step 7: Efficient representation of bit symbol: Each non-zero coefficient and each run of zero coefficients are replaced by Huffman code. The code is written in a file and they constitute the compressed modulated data.
2.2. Image decoding process The decoding process is just reverse to that of the encoding process. The inputs to the decoder are the modulated compressed Huffman bit sequence and the set of secret key (seed). This is the same key that is used at the time of watermark embedding for the generation of PRN (pseudo-random noise) matrix (code pattern)
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Pi . The output will be of good quality image if the extracted key is correct and vice versa. The steps for decoding process are described as follows. Step 1: Huffman decoding and key (W) extraction: The extraction of the access control key is nothing but the process of watermark decoding, which consists of the following steps. (A) Reconstruction of Cb component: Huffman decoding, reverse zigzag scan, denormalization, inverse DCT and up sampling are performed on the resultant quantized coefficients of the chrominance (Cb ) component in order to reconstruct this channel. (B) Watermark bit extraction from Cb component: Watermark bits are then extracted from ‘L’ number of non-overlapping blocks of same size (m0 m0 ), which is used during embedding. The typical block size considered here is (128 128) and DCT is performed for those blocks. Then a zero-lag correlation is
Fig. 5. Test images. (a) Pepper; (b) Lena; (c) House; (d) Water ; (e) Boat ; (f) Kid; (g) Opera; (h) Paper machine.
Fig. 6. (a) Decompressed test image, (b) decompressed watermarked image, (c) and (d) results of maximum modulation in the luminance (Y) component with ‘ T ¼ 1’ , (c) decoded image without key, (d) decoded image with the true key, (e) and (f) results of maximum modulation in the chrominance (Cr ) component ‘ T ¼ 1’ , (e) decoded image without key, (f) decoded image with the true key, (g) and (h) results of maximum modulation in both luminance (Y) and chrominance (Cr ) component ‘ T ¼ 1’ , (g) decoded image without key, (h) decoded image with the true key.
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Table 2 Results of images without watermarking and quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Lena House Water
0.23 0.30 0.38
34.20:1 26.26:1 20.90:1
30.20 27.46 28.24
0.80 0.85 0.83
Table 3 Results of images after watermarking but without quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Lena House Water
0.23 0.30 0.38
33.75:1 25.86:1 20.82:1
29.76 26.96 28.01
0.79 0.83 0.83
calculated between the DCT coefficients of the embedded block and the particular code pattern (Pi ) which is used at the time of the embedding. This correlation value acts as decision variable mi where
mi ¼ /Xe ; Pi Sð0Þ
ð6Þ
The zero inside the brackets indicates zero-lag. Then watermark ^ i ) is extracted by the following rules: bit (W ^ i ¼ 1 if mi 4 0 ð7Þ W 0 else ^ i ) is treated as access The extracted bit sequence of watermark (W control key which will be used for block based demodulation to reconstruct the better quality image. Step 2: Block based demodulation of luminance/chrominance red (Cr ) channel: Huffman decoding is performed on luminance/ ^ ) chrominance red (Cr ) channel. Depending on the key (W
Fig. 7. Results if only luminance(Y) component is modulated by the key with ‘ T ¼ 4’ , (a) and (b) quality accesses control for case-1 (100% coefficient), (a) decoded image without key, (b) decoded image with the true key, (c) and (d) quality accesses control for case-2 (90% coefficient), (c) decoded image without key, (d) decoded image with the true key, (e) and (f) accesses control for case-3 (80% coefficient), (e) decoded image without key, (f) decoded image with the true key, (g) and (h) accesses control for case-4 (70% coefficient), (g) decoded image without key, (h) decoded image with the true key.
Fig. 8. Results if only chrominance (Cr ) component is modulated by the key with ‘ T ¼ 4’ , (a) and (b) quality accesses control for case-1 (100% coefficient), (a) decoded image without key, (b) decoded image with the true key, (c) and (d) quality accesses control for case-2 (90% coefficient), (c) decoded image without key, (d) decoded image with the true key, (e) and (f) accesses control for case-3 (80% coefficient), (e) decoded image without key, (f) decoded image with the true key, (g) and (h) accesses control for case-4 (70% coefficient), (g) decoded image without key, (h) decoded image with the true key.
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Fig. 9. Results if both luminance (Y) and chrominance (Cr ) component is modulated by the key with ‘ T ¼ 4’ , (a) and (b) quality accesses control for case-1 (100% coefficient), (a) decoded image without key, (b) decoded image with the true key, (c) and (d) quality accesses control for case-2 (90% coefficient), (c) decoded image without key, (d) decoded image with the true key, (e) and (f) accesses control for case-3 (80% coefficient), (e) decoded image without key, (f) decoded image with the true key, (g) and (h) accesses control for case-4 (70% coefficient), (g) decoded image without key, (h) decoded image with the true key.
Table 4 Results of images if only luminance (Y) component is modulated by the key and decoded without and with the true key for T ¼ 4. % of coeff.
Images
Pepper
Lena
Water
House
100 (WOK)
PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSI BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSI PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM
20.34 0.47 0.25 32.00:1 26.34 0.72 23.07 0.55 0.25 32.00:1 26.34 0.72 23.86 0.58 0.25 32.00:1 26.34 0.72 24.30 0.61 0.25 32.00:1 26.34 0.72
21.17 0.50 0.24 33.70:1 29.76 0.80 23.73 0.57 0.24 33.70:1 29.76 0.80 25.06 0.63 0.24 33.70:1 29.76 0.80 26.06 0.66 0.24 33.70:1 29.76 0.80
18.62 0.29 0.38 20.80:1 28.01 0.83 20.52 0.41 0.38 20.80:1 28.01 0.83 21.94 0.49 0.38 20.80:1 28.01 0.83 22.78 0.55 0.38 20.80:1 28.01 0.83
17.99 0.41 0.31 25.80:1 26.96 0.83 20.99 0.54 0.31 25.80:1 26.96 0.83 22.39 0.61 0.31 25.80:1 26.96 0.83 22.79 0.65 0.31 25.80:1 26.96 0.83
100 (WK) 90 (WOK)
90 (WK) 80 (WOK)
80 (WK) Fig. 10. Results of PSNR for different cases tested for large number of images. 70 (WOK)
extracted in step 1, percentage of coefficients (x%) and the coefficients of the particular channels to be demodulated are determined. If the number of non-zero AC coefficients in a block (8 8) is greater than a predefined threshold (T), ‘x’% of the nonzero AC coefficients are selected starting from the end-of-block (EOB). The coefficients are demodulated pseudo-randomly depending on the sub-key (S). The demodulation is performed according to the following rule: C el ¼ ð1Þ C e e
ð8Þ
70 (WK)
WOK: without key, WK: with true key.
Step 4: Inverse color space transformation: The YC b Cr color components are transformed to get RGB image using the following rule: R ¼ Y þ1:371ðCb 128Þ
el
where C and C are the quantized DCT coefficients before and after demodulation process, respectively. Step 3: Inverse transformation of luminance/chrominance (Cr ) channel coefficients: The reverse zigzag scan, denormalization, inverse DCT and up sampling (for Cr only) are performed on the resultant quantized coefficients found in step 2 in order to reconstruct the luminance/chrominance (Cr ) component.
G ¼ Y 0:698ðCr 128Þ 0:336ðCb 128Þ B ¼ Y þ 1:732ðCb 128Þ
ð9Þ
The authorized users thus access relatively good quality images depending on the subscription agreement. The quality of the images is also determined by the faithful decoding of the
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embedded watermark, the amount and the group of coefficients of the particular channel to be modulated, channel used for watermarking, knowledge of the key, etc. Performance of the proposed quality access control is presented now in the next section.
3. Performance evaluation The performance of the proposed access control scheme is tested over large number of benchmark images [18,19]. All of the Table 5 Results of images if only chrominance (Cr ) component is modulated by the key and decoded without and with the true key for T ¼ 4. % of coeff.
Images
Pepper
Lena
Water
House
100 (WOK)
PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM
23.26 0.66 0.25 32.00:1 26.34 0.72 24.68 0.68 0.25 32.00:1 26.34 0.72 24.92 0.68 0.25 32.00:1 26.34 0.72 25.18 0.68 0.25 32.00:1 26.34 0.72
27.97 0.77 0.24 33.70:1 29.76 0.80 28.27 0.78 0.24 33.70:1 29.76 0.80 28.61 0.78 0.24 33.70:1 29.76 0.80 28.61 0.80 0.24 33.70:1 29.76 0.80
26.49 0.81 0.38 20.8:1 28.01 0.84 26.93 0.82 0.38 20.80:1 28.01 0.84 27.11 0.82 0.38 20.80:1 28.01 0.84 27.12 0.82 0.38 20.80:1 28.01 0.84
25.72 0.81 0.31 25.80:1 26.96 0.83 26.10 0.82 0.31 25.80:1 26.96 0.83 26.20 0.82 0.31 25.80:1 26.96 0.83 26.25 0.82 0.31 25.80:1 26.96 0.84
100 (WK) 90 (WOK)
90 (WK) 80 (WOK)
80 (WK) 70 (WOK)
70 (WK)
WOK: without key, WK: with true key.
Table 6 Results of images if both luminance (Y) and chrominance (Cr ) components are modulated by the key and decoded without and with the true key for T ¼ 4.
test images are of size (512 512), 24-bit/pixel-color RGB image and some of them are shown in Fig. 5. The present study uses peak-signal-to-noise-ratio (PSNR) [20] and mean-structuresimilarity-index-measure (MSSIM) (calculated by the average value of Structural SIMilarity (SSIM) Index of the three-color channels) [21] to quantify the distortion measure as well as quality of the images. We have used sampling rate of type 4:2:0 as it is most commonly used in all compression schemes. Fig. 6(a) shows the decompressed ‘Pepper’ image with various quality measures such as (PSNR 26.77 dB, MSSIM 0.73, bit rate 0.24 bits/pixel, compression ratio 32.56:1), obtained after decompression operation. Table 2 lists the bit rate (BR), compression ratio (CR), PSNR and MSSIM value for some images. Fig. 6(b) shows the decompressed ‘Pepper’ image (PSNR 26.34 dB, MSSIM 0.71, BR 0.24 bits/pixel, CR 32.00:1), after watermarking but without applying proposed quality access control mechanism. Table 3 lists the BR, CR, PSNR and MSSIM values for some images under similar situation.
Table 7 Results of images without watermarking and quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Boat Kid Opera Paper machine
0.32 0.19 0.24 0.29
24.85:1 40.46:1 32.21:1 26.97:1
26.01 33.48 32.53 32.29
0.75 0.90 0.89 0.91
Table 8 Results of images with watermarking but without quality access control mechanism. Image
BR
CR
PSNR (dB)
MSSIM
Boat Kid Opera Paper machine
0.32 0.20 0.25 0.30
24.69:1 39.95:1 31.79:1 26.77:1
25.75 32.47 31.27 31.16
0.74 0.89 0.87 0.90
Table 9 Results of images when 90% of non-zero AC coefficient in a block is modulated by the key and decoded without and with the true key for T ¼ 4.
% of coeff.
Images
Pepper
Lena
Water
House
Channel
100 (WOK)
PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM PSNR (dB) MSSIM BR CR PSNR (dB) MSSIM
19.38 0.44 0.25 32.00:1 26.34 0.72 22.22 0.53 0.25 32.00:1 26.34 0.72 23.02 0.56 0.25 32.00:1 26.34 0.72 23.54 0.58 0.25 32.00:1 26.34 0.72
20.89 0.48 0.24 33.70:1 29.76 0.80 23.32 0.56 0.24 33.70:1 29.76 0.80 24.64 0.61 0.24 33.70:1 29.76 0.80 25.54 0.64 0.24 33.70:1 29.76 0.80
18.40 0.28 0.38 20.80:1 28.01 0.83 20.30 0.40 0.38 20.80:1 28.01 0.83 21.69 0.49 0.38 20.80:1 28.01 0.83 22.48 0.53 0.38 20.80:1 28.01 0.83
17.82 0.40 0.31 25.80:1 26.96 0.83 20.76 0.53 0.31 25.80:1 26.96 0.83 22.10 0.60 0.31 25.80:1 26.96 0.83 22.50 0.64 0.31 25.80:1 26.96 0.83
Y (WOK) PSNR (dB) MSSIM BR CR Y (WK) PSNR (dB) MSSIM Cr (WOK) PSNR (dB) MSSIM BR CR Cr (WK) PSNR (dB) MSSIM Y and Cb (WOK) PSNR (dB) MSSIM BR CR Y and Cb (WK) PSNR (dB) MSSIM
100 (WK) 90 (WOK)
90 (WK) 80 (WOK)
80 (WK) 70 (WOK)
70 (WK)
WOK: without key, WK: with true key.
Boat
Kid
Opera
Paper m/c
20.70 0.49 0.32 24.60:1
25.671 0.71 0.20 39.90:1
23.86 0.58 0.25 31.70:1
22.16 0.60 0.30 26.70:1
25.75 0.75
32.47 0.90
31.27 0.88
31.16 0.91
24.50 0.72 0.32 24.60:1
31.49 0.88 0.20 39.90:1
30.42 0.87 0.25 31.70:1
30.13 0.90 0.30 26.70:1
25.75 0.75
32.47 0.89
31.27 0.87
31.16 0.91
20.30 0.48 0.32 24.60:1
25.44 0.71 0.20 39.90:1
23.68 0.58 0.25 31.70:1
22.01 0.59 0.30 26.70:1
25.75 0.75
32.47 0.89
31.27 0.87
31.16 0.91
WOK: without key, WK: with true key.
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Fig. 11. (a) Decompressed test image, (b) decompressed watermarked image, (c) and (d) results of modulation in only luminance (Y) component for case-2 (90% coefficient) with ‘ T ¼ 4’ , (c) decoded image without key, (d) decoded image with the true key, (e) and (f) results of modulation in only chrominance (Cr ) component for case-2 (90% coefficient) with ‘ T ¼ 4’ , (e) decoded image without key, (f) decoded image with the true key, (g) and (h) results of modulation in both luminance (Y) and chrominance (Cr ) component for case-2 (90% coefficient) with ‘ T ¼ 4’ , (g) decoded image without key, (h) decoded image with the true key.
Table 10 Comparison of results: coding rate, KCR, PSNR (dB) and MSSIM. Coding rate (bits/pixel)
Chang [8] Zaidee [22] Proposed
8 1.24 0.24
KCR (%)
BER ¼ 104
BER ¼ 104
BER ¼ 105
BER ¼ 106
PSNR (dB)
MSSIM
76 99 100
99 99 100
100 100 100
35.74 28.84 35.17
0.962 0.910 0.961
It is clear from the results shown in Tables 2 and 3 that the watermarking mechanism, on an average, decreases about 1.12% CR leading to its little impact on data rate. Fig. 6(c, e, g) show the modulated ‘Pepper’ image under T ¼ 1 i.e. when all non-zero AC coefficients of the various channels are modified. Fig. 6(d, f, h) show the decoded image with the true access control key. The distortion is large as most of the energy of a block is concentrated near the direct current (DC) coefficient and are modulated when we take T ¼ 1. We also conduct the similar types of tests by setting the parameter value T ¼ 4. Figs. 7–9 show the results of ‘Pepper’ image if only Y, only Cr and both Y and Cr channels are modulated by the key and are decoded with and without the true key, respectively. In this study, the amount of non-zero AC coefficients to be modulated are of four categories i.e. case-1: 100%, case-2: 90%, case-3: 80%, case-4: 70%. It is seen from Figs. 7–9 that in all test cases decoded ‘Pepper’ images with the true key are of ultimate quality. The decoded images without the proper key produces a lower level of quality. In other words, the images of Figs. 7(a, c, e, g)–9(a, c, e, g) will be available to all users but the images of Figs. 7(b, d, f, h)–9(b, d, f, h) will only be available to the authorized users who have the subscription agreement. Fig. 10 shows the variation of PSNR graphically for the modulation of different channels. The modulation is done for different percentage of coefficients and images are decoded without and with the true key. The numerical values shown in the graphical representation are obtained by taking average for the PSNR values obtained from the test done over large number of
Table 11 Robustness (in term of BER) against various filtering operations. Filter type
Median Wiener Mean Highpass
Mask size 33
55
77
99
11 11
0 0 0 0
0 0 0 0
0.12 0 0.12 0
0.25 0 0.31 0
0.31 0 0.43 0
images. Tables 4–6 list the BR, CR, PSNR and MSSIM values for various images like Pepper, Lena, Water and House. It is clear from the results of Table 5 that modulation of only Cr component is not sufficient to implement efficient access control, as that amount of modulation does not distort signal very much. It is clear from the results shown in Tables 4–6 that for all types of experimentations, modulation process is reverted completely and the full quality of the image is achieved without increasing any bit rate. After studying the results of Tables 4–6, we can conclude that modulation of non-zero AC coefficient by an amount of 90% is enough for access control, as that amount of distortion is sufficient for quality access control of image. By setting this parameter (i.e. percentage of non-zero AC coefficient to be modulated in a block is of 90% and T ¼ 4), we test our scheme for other test images. The results obtained are shown in Tables 7–9. Fig. 11 shows the results of the test image ‘Boat sailing’.
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Table 12 Experimental results with StirMark 4.0. Strength
BER
MEDIAN_5 MEDIAN_7 MEDIAN_9 ROTSCALE_0.25 ROTCROP_0.25 SS_1 SS_2 SS_2 CROPPING_20 CROPPING_50 LATESTRNDDIST_1 LATESTRNDDIST_0.33 REMOV_LINES_50 REMOV_LINES_70 NOISE_60 AFFINE_4 AFFINE_6 CONV_1 CONV_2
0 0.06 0.25 0.25 0.12 0 0 0 0.27 0.27 0.43 0.33 0 0 0 0.25 0.12 0 0
Robustness performance of the proposed scheme against various signal processing operations, such as format changes or transmission over noisy channels is examined. Experimental results show that our scheme is robust to such operations, as one bit of watermark is embedded by spreading over a block of size 128 128 using SS modulation. The robustness of the proposed scheme is also tested against the format change that leads to the change of the color space. The watermark is embedded in the YC b Cr color space and is detected efficiently after transforming it from YC b Cr to RGB and then from RGB to YC b Cr . It ensures that the scheme is robust to color space conversion. The robustness of the scheme over noisy channels is also tested and the result is compared with the results obtained in [8,22] in terms of coding rate, KCR (key correction ratio), PSNR, MSSIM, BER (bit error rate). The word BER here implies the error in the received compressed signal when transmitted through the noisy channel or manipulated by some user. The BER is simulated in this experimentation by randomly manipulating the compressed data. This affects PSNR and MSSIM values of the received image with respect to the compressed version. The term KCR may be defined as the percentage of match between the true key and the extracted key. It is observed from the results shown in Table 10 that the proposed scheme can extract the key correctly (high value of KCR) even with the presence of various bit error. The scheme also decodes the image effectively without increasing overhead than the existing schemes [8,22]. Simulation results show that our method can decode the key with greater reliability at BER of 104 and offers better PSNR and MSSIM values at low coding rate. It is also to be pointed out that PSNR for Chang [8] method is little better compared to our method but the coding rate of the former is much higher than the latter. This in other words show that high PSNR in [8] is possible when there is no compression while our method allows significant compression (low bit rate) even at reasonable quality. Similarly, we also compare the result of the proposed access control method with the work reported in [22]. Results shown in Table 10 highlights the superiority of our method. Robustness performance of the proposed method is also studied against the standard signal processing operations like median filtering, Wiener filtering, mean filtering, highpass filtering, scale down by factor 0.5, dynamic range change (50–200), histogram equalization, etc. The results obtained for filtering operations are shown in Table 11. It is seen that the BER values for
the extracted watermarks due to all standard signal processing operations are quite low even for the mask size (11 11). This is quite natural as the proposed scheme uses SS modulation for data hiding and one bit of watermark is embedded in a block of size 128 128. We also test performance of the proposed scheme against benchmark StirMark 4.0 [23–25]. Table 12 lists the experimental results using StirMark 4.0. The execution time of the proposed scheme is studied and it is seen that the scheme takes on an average 63.05 s (image encoding: 41.71 s; image decoding: 21.34 s) for both image encoding and decoding process. The simulation is conducted on Pentium IV, 2.80 GHz processor, with 504 MB RAM using MATLAB 7 version.
4. Conclusions and scope of future works This paper proposes an efficient quality access control scheme of color images in compressed domain. The users with the complete knowledge of the key can recover the full quality of the image, whereas the other users can only access to a low quality version of it. The secret key that is used for modulation is embedded in the cover image as a watermark using spread spectrum (SS) modulation. Experimental results show that this lowers the risk of losing the information of the secret key during format conversion or due to error in transmission. Moreover, the secret key is totally synchronized to the content. Future work may be explored for the extension of the proposed access control scheme on video and sound data. References [1] Imaizumi S, Watanabe O, Fujiyoshi M, Kiya H. Generalized hierarchical encryption of JPEG 2000 code streams for access control. In: Proceeding of IEEE international conference on image processing, 2005. p. 1094–7. [2] Grosbois R, Gerbelot P, Ebrahimi T. Authentication and access control in the jpeg 2000 compressed domain. In: Proceeding of SPIE 46th annual meeting, applications of digital image processing, 2001. p. 95–104. [3] Liu JL. Efficient selective encryption for JPEG 2000 images using private initial table. Pattern Recognition 2006;39:509–1517. [4] Won YG, Bae TM, Ro YM. Scalable protection and access control in full scalable video coding. In: Lecture Notes in Computer Science, vol. 4283. Berlin: Springer; 2006. p. 407–21. [5] Bertino E, Fan J, Ferrari E, Hacid MS, Elmagarmid AK, Zhu X. A hierarchical access control model for video database systems. ACM Transactions on Information Systems 2003;21:155–91. [6] Wen JG, Severa M, Zeng W, Luttrell MH, Jin W. A format-compliant configurable encryption framework for access control of video. IEEE Transactions on Circuits and Systems for Video Technology 2002;12:545–57. [7] Pickering M, Coria LE, Nasiopoulos P. A novel blind video watermarking scheme for access control using complex wavelets. In: Proceeding of IEEE international conference on consumer electronics, 2007. p. 1–2. [8] Chang FC, Huang HC, Hang HM. Layered access control schemes on watermarked scalable media. Journal of VLSI Signal Processing 2007;49:443–55. [9] Xu X, Dexter S, Eskicioglu AM. A hybrid scheme for encryption and watermarking. In: Proceeding of IS & T/SPIE symposium electronic imaging, 2004. p. 723–34. [10] Petitcolas FAP. Digital watermarking. Berlin: Springer; 2003. [11] Maity SP, Kundu MK, Maity S. Dual purpose FWT domain spread spectrum image watermarking in real-time. International Journal of Computer & Electrical Engineering 2009;35(2):415–33 [special issue on Real-time security and copyright protection of multimedia]. [12] Campisi P, Carli M, Giunta G, Neri A. Blind quality assessment system for multimedia communications using tracing watermarking. IEEE Transaction on Signal Processing 2003;51:996–1002. [13] Phadikar A, Kundu MK, Maity SP. Quality access control of a compressed gray scale image. In: Proceeding of national conference on computer vision, pattern recognition. Image processing and graphics (NCVPRIPG 08), 2008. p. 13–19. [14] Wee SJ, Apostolopoulos JG. Secure scalable streaming enabling transcoding without decryption. In: Proceeding of IEEE international conference on image processing, 2001. p. 437–40. [15] Maity SP, Kundu MK, Das TS. Robust SS watermarking with improved capacity. Pattern Recognition Letters 2007;28:350–6. [16] Solomon D. Data compression: the complete reference. London: Springer; 2007.
ARTICLE IN PRESS ¨ ) 64 (2010) 833–843 A. Phadikar, S.P. Maity / Int. J. Electron. Commun. (AEU
[17] Phadikar A, Maity SP. ROl based quality access control of compressed color image using DWT via lifting. Electronic Letter on Computer Vision and Image Analysis 2009;8:51–67. [18] /http://www.cl.cam.ac.uk/fapp2/watermarkingS. [19] /http://www.petitcolas.net/fabien/watermarking/image_database/index. htmlS. [20] Gonzalez RC, Woods RE. Digital image processing using Matlab. Upper Saddle River, NJ: Prentice-Hall; 2005. [21] Wang ZA, Bovik C, Sheikh HR, Simoncelli EP. Image quality assessment: from error measurement to structural similarity. IEEE Transactions on Image Processing 2004;13:1–14. [22] Zaidee AA, Fazdliana S, Adznan BJ. Content access control for JPEG images using CRND zigzag scanning and QBP. In: Proceedings of the sixth IEEE international conference on computer and information technology (CIT’06), 2006. p. 146. [23] Petitcolas FAP. Watermarking schemes evaluation. IEEE Signal Processing 2000;17:58–64. [24] Petitcolas FAP, Anderson RJ, Kuhn MG. Attacks on copyright marking systems. In: Proceeding of second international workshop on information hiding. Lecture Notes in Computer Science, vol. 1525. Berlin: Springer; 1998. p. 219– 39. [25] /http://www.petitcolas.net/fabien/watermarking/stirmark/S.
Amit Phadikar received his B.E. in Computer Science and Engineering in 2002 from Vidyasagar University, W.B., India and M.Tech degree in Information Technology (Artificial Intelligence) in 2005 from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal, M.P., India. Since 2005 he is working as Lecturer at the Department of Information Technology, MCKV Institute of Engineering, Liluha, Howrah, W.B., India. His research areas include digital image watermarking, digital signal processing, content based image retrieval. Currently he is working towards his Ph.D. degree in Engineering (Information Technology) from Bengal Engineering and Science University, Shibpur, India.
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Santi P. Maity received his B.E. in Electronics and Communication Engineering and M.Tech degree in Microwaves, both from the University of Burdwan, India, in the year 1993 and 1997, respectively. He received his Ph.D. degree in Engineering (Computer Science and Technology) from Bengal Engineering and Science University, Shibpur, India, in the year 2008. During January 2009 to July 2009 he did his posdoctoral work concerning watermarking in lured applications in the ‘Laboratoire des Signaux et Systems (CNRS-Supelec-Universite Paris-Sud 11)’ in France. He is at present working as Assistant Professor at the Department of Information Technology, Bengal Engineering and Science University, Shibpur and acted as Head of the Department from September 2008 to January 2009. He also worked as Lecturer in Electronics and Telecommunication Engineering Department of the same university from 2000 to 2006. Prior to that, he worked as Lecturer in Electronics and Telecommunication Engineering Department of K.G. Engineering Institute, Bishnupur, Bankura, India and Haldia Institute of Technology, Haldia, India, from 1997 to 2000. His research areas include digital image watermarking, multiuser detection in CDMA, digital signal processing, digital wireless communication, VLSI watermarking. He delivers several lectures and invited talk on short term course/seminar/workshop/ conference and acts as committee member for national and international conferences like IMSA 2008, IMSA2009 and IMSA 2010. He is an Associate Member of Institute of Engineer (India) and a Member of Institute of Electronics and Telecommunication Engineers (IETE), India. He is principal investigator of a project ‘High Power and Spectral Efficiency Multiuser System for Broadband Wireless Communication’ funded by Department of Information Technology, Ministry of Communication and Information Technology, Government of India. He has contributed about 70 research papers in well-known and prestigious archival journals, international refereed conferences and as chapters in edited volumes.