Robust and secured image-adaptive data hiding

Digital Signal Processing 22 (2012) 314–323

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Digital Signal Processing www.elsevier.com/locate/dsp

Robust and secured image-adaptive data hiding Suresh N. Mali ∗ , Pradeep M. Patil, Rajesh M. Jalnekar Vishwakarma Institute of Technology, Pune, (MS), India

a r t i c l e

i n f o

a b s t r a c t

Article history: Available online 29 September 2011 Keywords: Steganography Data hiding Robustness Security Energy AET Quantization CDCS PSNR IQMs

Rapid growth in the demand and consumption of digital information in past decade has led to valid concerns over issues such as content security, authenticity and digital right management. Imperceptible data hiding in digital images is an excellent example of demonstration of handling these issues. Classical Cryptography is related with concealing the content of messages, whereas, Steganography is related with concealing the existence of communication by hiding the messages in cover. This paper presents a robust and secured method of embedding high volume of text information in digital Cover-images without incurring any perceptual distortion. It is robust against intentional or unintentional attacks such as image compression, tampering, resizing, filtering and Additive White Gaussian Noise (AWGN). The schemes available in the literature can deal with these attacks individually, whereas the proposed work is a single methodology that can survive all these attacks. Image Adaptive Energy Thresholding (AET) is used while selecting the embedding locations in frequency domain. Coding framework with Class Dependent Coding Scheme (CDCS) along with redundancy and interleaving of embedded information gives enhancement in data hiding capacity. Perceptual quality of images after data hiding has been tested using Peak Signal to Noise Ratio (PSNR) whereas statistical variations in selected Image Quality Measures (IQMs) are observed with respect to Steganalysis. The results have been compared with existing algorithms like STOOL in spatial domain, COX in DCT domain and CDMA in DWT domain. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Due to availability of Internet throughout the world, in underdeveloped as well as developed countries, content security is playing a major role in multimedia communication. Since the same Internet channels are used for commercial activities, coding the information before transmitting has become a common practice to overcome hacking problems. The techniques available to achieve the goal of content security are Cryptography, Encryption and Steganography. Cryptography scrambles the message so that it cannot be understood, while Steganography hides the very existence of the message by carefully embedding it into a cover. An eavesdropper can intercept a Cryptographic message but one may not even know the existence of Steganographic communication. Encryption and Steganography achieves the same goal via different means. Encryption encodes the data so that an unintended recipient cannot determine its intended meaning. Steganography, in contrast attempts to prevent an unintended recipient from suspecting about the hidden information [1,2]. Combining Encryption with Steganography allows better private communication.

*

Corresponding author. E-mail addresses: [email protected] (S.N. Mali), [email protected] (P.M. Patil), [email protected] (R.M. Jalnekar). 1051-2004/$ – see front matter doi:10.1016/j.dsp.2011.09.003

© 2011

Elsevier Inc. All rights reserved.

Image Steganography is the art and science of hiding important (secret) information in a Cover-image. The word Steganography has been derived from the Greek words “stegos” meaning “cover” and “grafia” meaning “writing” [2] referred as “covered writing”. Security is a major consideration while embedding messages of large volume. There are several directions to alleviate this security issue: some involves adding uncertainty to the embedding mechanism, some generates features with randomness such as projecting a set of media components onto proprietary directions [3], and some focuses on making the embedded message to be tamper-proof and forge-proof. In this work, main focus is given on adding security to the core embedding mechanism to make it difficult for an attacker to detect the existence of evidence of embedding. The work presented in this paper concentrates on embedding the text messages into images, however, the proposed approach and analysis can be easily applied for embedding the message into audio or video signals as a cover. An early work on the image Steganography is Least Significant Bit (LSB) technique [4–9] that attempts to minimize the detectability of hidden data by introducing as little distortion as possible during embedding. However, as pointed out by Fridrich and Goljan [9,10], recent advances in Steganalysis have shown that this approach does not guarantee detectability, evident by the fact that they can be successfully attacked using statistical or even visual attacks [11].

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Steganography Tool (STOOL) for windows [11,12] is one of the most popular technique available as an open source. Version 3.0 includes programs that process GIF and BMP images. A useful feature of STOOL is a status line that displays the largest message size that can be embedded in the given cover file. EzStego [11], a JAVA based tool is limited to 8-bit GIF files. This is an application of Steganography, on line stego tool which runs on different platforms and on internet. Hide and Seek 4.1 by Colin Maroney [4,13,14] is another basic Steganography program that works on either 8-bit color or 8-bit black-and-white GIF files. If the cover image is larger, the stego-image will be cropped to fit. Hide4PGP is a freeware program distributed as source code in ANSI C and precompiled executables for DOS, OS/2 and the Win32 console [15]. The information to be hidden is scrambled, to prevent perceptible patterns with repetitive data in BMP image files. White Noise Storm (WNS) [16] is a set of software for DOS which applies LSB technique to embed the encrypted message. It also utilizes the randomization and spread spectrum principle which scatters the hidden message throughout the image. Steganos is one of the most impressive Steganography tool developed over the past few years [17]. This tool has changed from being a program to embed the message in LSBs of images to a sophisticated commercial Steganography suit with capability of employing adaptive Steganography. Westfeld proposed a LSB based JPEG Steganography scheme, named F5 [18]. In this technique, instead of replacing the LSB, the DCT coefficients are either increased or decreased by one. COX is a secured (tamper resistant) algorithm for watermarking images in DCT domain [6] which advocate a watermark embedded imperceptibly using spread-spectrum-like fashion into the perceptually most significant spectral components of the Cover-image. Data hiding capacity is increased using Code Division Multiple Access (CDMA) DWT based technique [19–21], however the perceptual quality of the image degrades after embedding the information. Solanki et al. [22] proposed a method called Selective Embedding Coefficient (SEC) for the transmitter which employs local criteria to select coefficients for embedding. Use of local criteria for deciding where to embed is found to be crucial for maintaining image quality under high volume embedding. The blocks whose energy is greater than a predefined threshold are selected for information embedding. There is a trade off between capacity, robustness (against attacks) and embedding induced distortion. In this work, along with these three parameters a fourth parameter is considered which is the security of a hidden information. Specifically, a mechanism of CDCS to increase data hiding capacity and AET along with data redundancy, interleaving and randomization to increase the robustness and security of the hidden information. The effect AET and Quality Factor (QF) on PSNR has also been tested. Robustness of the AET method is tested under various intentional and unintentional attacks such as image compression, limited amount of local and global image tampering, image resizing, low pass filtering and AWGN. The rest of the paper is organized as follows. Section 2 deals with the design issues while embedding large volume of text in Cover-images. Section 3 describes the proposed robust and secured image-adaptive data hiding system in detail. Results and discussions are compiled in Section 4 at length followed by conclusions in Section 5. 2. Design issues The design issues while hiding the text information in images using sealed Steganography are: 1. The Stego image should not have any distortion artifacts that may cause any visual inspection to trigger the detection of hidden information.

315

Fig. 1. Steganographic design issues.

Fig. 2. Trade-off between capacity, imperceptibility and robustness.

2. The algorithm should be statistically undetectable and it must provide Robustness against a variety of image manipulation attacks. 3. The information embedded should be highly secured. 4. The most important is the algorithm should not scarify the embedding Capacity in order to achieve the said requirements. All these design issues are briefly summarized in Fig. 1. There are four major requirements of information hiding depending upon the purpose of the application. There is always a trade-off between these three main parameters i.e. capacity, imperceptibility and robustness as shown in Fig. 2. If any one of these parameters is changed then the other two gets affected. Though the capacity, robustness, and security relation issues are driven by the application need and its priorities, one has to optimize all the parameters to get the best results. 3. Proposed system The general Steganographic data hiding system is as shown in Fig. 3. To break the security of the communication the attacker is going to continuously monitor the public channel. A passive attacker is interested in finding whether a Stego-image sent by the transmitter to the receiver contains any secret information or not. Therefore, the main role of proposed embedding algorithm (Fig. 4) is to reduce the perceptual degradation of a Stego-image so as not to arouse an attacker’s suspicion. In other words, a Steganographic system is considered to be insecure, if anyone is able to differentiate between Cover-image and Stego-image. Embedding of text message in Cover-image leaves unique artifacts in Stego-image, which can be detected using either PSNR or IQMs. The proposed Steganographic data hiding system as shown in Fig. 4, consists of text processing and image processing phases. In “Text Processing Phase” the text message is processed and in “Image Processing Phase” the Cover-image is processed. After embedding the information bits into the Cover-image, it is reconstructed to get

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character codes are used which needs 7-bit to represent each character of the text message. However, in order to accommodate more and more characters and increase the data hiding capacity we propose a new CDCS encryption. After studying various text one can assigned fixed decimal codes using proposed CDCS technique to each character by considering their relative frequency of occurrences [74]. One can categorize the characters in three different non-overlapping classes as Class A (most frequently appearing characters), Class B (average frequently appearing characters) and Class C (less frequently appearing characters). Further, assuming only capital letters, alphanumeric and few special characters the number of bits needed to represent each character can be reduced in each class. If N 1 , N 2 and N 3 are the total number of characters belonging to Class A, Class B and Class C respectively in CDCS, Total number of bits m to be embedded is given by, Fig. 3. General Steganographic data hiding system.

a Stego-image as an output. The inputs to the proposed system are Cover-image file (C ), Text message file (m), Number of Redundant bits for each encrypted bits (r), Number of bits for interleaving the ˆ Seed for the generabit stream (n), Energy Threshold Factor ( w), tion of random number (seed) and image QF. 3.1. Text Processing Phase Text message file along with r and n are the inputs to this phase. The steps carried out during this phase are: Step 1: Step 2: Step 3: Step 4:

Read the text message file. Encrypt the characters of the text message file. Add redundancy to each of the encrypted bit. Interleave the bits and read them column wise to make the bit stream ready for embedding.

The encryption methodology adopted for encrypting text characters plays a vital role in deciding the embedding capacity and the level of robustness and security of the entire Steganographic system. In most of the Steganographic systems in the literature ASCII

m = (N1 + 2 × N2 + 2 × N3 ) + 4 × h

(1)

where h = N 1 + N 2 + N 3 , i.e. total number of characters in a text file. Percentage Bit Saving (PBS) is given by,

 PBS = 1 −



m 7×h

 × 100%

(2)

where m = Total number of bits to be embedded Saving number of encoding bits is nothing but increase in data hiding capacity. The set (T t ) of text characters representing the text of the message to be embedded is given by,

T t ∈ {t 1 , t 2 , t 3 , t 4 , . . . , th }

(3)

where h = Total number of characters in the given text. The set (T e ) of encrypted bits using CDCS is given by,

T e ∈ {e 1 , e 2 , e 3 , e 4 , . . . , em }

(4)

where m = Total number of bits to be embedded. Along with CDCS we are also using redundancy and interleaving of embedded bits which will increase robustness of the system for attacks like image tampering and AWGN. With the help of redundancy one can make the copies of embedded bits and using

Fig. 4. Proposed Steganographic embedding algorithm.

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interleaving these bits scatters all over the Stego-image. Therefore, although some part of the Stego-image gets tampered or changed due to AWGN, another copy of tampered information bits will always be available in rest part of the Stego-image. If r is the number of bits that are to be repeated for each embedded bit, then the set (T er ) of encrypted bits after adding redundancy is given by,

T er ∈ {e 11 , e 12 , ..e 1r , e 21 , e 22 , ..e 2r , e 31 , e 32 , ..e 3r , . . . ., em1 , em2 , ..emr } For the sake of simplicity consider a set (M 

(5)

= T er ) having one-

to-one correspondence as,

M  ∈ {b1 , b2 , b3 , b4 , . . . , b p }

(6)

where b1 = e 11 and b p = emr . Before embedding the bits the set (M  ) is arranged on the basis of interleaving factor (n) as,

M  ∈ {b1 , b2 , b3 , . . . , bn , bn+1 , bn+2 , bn+3 , . . . , b2n , . . . , b p }

(7)

While embedding the bit stream M  is referred as M and is given by,

M ∈ {b1 , bn+1 , b2n+1 , . . . , b2 , bn+2 , b2n+2 , . . . , b3 , bn+3 , b2n+3 , . . . , b p }

(8)

Bits taken in this specified sequence are called as Final Bit Stream (FBS) which eventually gets embedded into the quantized DCT coefficients of the selected image blocks in image processing phase. More the value of redundancy, less will be the error in recovering the information at the receiving end. However, it will reduce the number of characters to be embedded. More the value of redundancy, more will the redundant bits gets spread all over the image. Both redundancy and interleaving are responsible for the robust data recovery at the receiver end. 3.2. Image processing phase This phase take the Cover-image file, Energy Threshold Facˆ Seed for the generation of random number (seed) and tor ( w), required QF as an input and select the DCT coefficients for embedding. The steps carried out during this phase are: Step 1: Read the Cover-image given by the user. Step 2: Divide the image into 8 × 8 non-overlapping blocks. Step 3: Apply two dimensional DCT to each block. If the intensity values of the 8 × 8 blocks are ai j , then the corresponding DCT coefficients c i j are given by,

c i j = DCT 2 (ai j )

(9)

where DCT 2 denotes a two-dimensional DCT and i , j = {0, 1, . . . , 7}. Step 4: Calculate Energy of each block. Energy of a block is computed using,

E=

7  7 

C i j 2 ,

317

ˆ from the user and calculate Mean Value of EnStep 5: Accept w ergy (MVE) using, MVE =

B 1

B

Eb

(11)

1

where B = Total number of blocks abd b = block number. Step 6: Identify the Valid Blocks VBs which satisfies the Energy ˆ × MVE. Threshold Criteria E  E t , where E t = w Step 7: Randomly Selects blocks from VBs using,

X n+1 = ( A × X n + C ) mod ( M ),

n0

(12)

where M is the modulus (M > 0), A is the multiplier (0  A < M), C is the increment (0  C < M), X 0 is the starting value 0  X 0 < M. Pseudo Random Sequence for specific choices of M, A, C and X 0 is given by (12). Step 8: Accept QF from the user and quantize the coefficients of all VBs by dividing them with respective elements of quantization matrix as,

Cˆ i j =

Cij QF

Mi j

∀ i , j = { 0, 1 , . . . , 7 }

(13)

where, Cˆ i j is the quantized coefficient matrix, M i j is the i jth element of quantization matrix for a given value of QF. Step 9: Identify the Valid DCT Coefficients (VCs) which satisfies the non-zero criteria (C i j = 0) and falls into lower and middle frequency band. Step 10: Check suitability of the given Cover-image (number of bits in FBS  number of VCs) and prompt the message on console “Given Cover-image is not suitable for embedding”. Step 11: Embed FBS given by the text processing phase into all selected VCs. The coefficients are scanned in zig-zag fashion, as in JPEG, to get one dimensional vector Cˆk . The embedding makes the quantized non-zero DCT coefficient ‘Odd’ for ‘bit = 0’ or ‘Even’ for ‘bit = 1’. The coefficients with hidden bits dˆk are given by, QF

 Odd Cˆ k , if bit = 0, ˆ dk = Even Cˆ k , if bit = 1

(14)

Step 12: The hidden coefficients dˆ k are reverse scanned to form an 8 × 8 matrix and multiplied by the JPEG Quantization matrix to obtain unquantified coefficients (C i j ). Step 13: Apply inverse DCT to each block. Step 14: Reconstruct the image as Stego-image. Low and middle frequency DCT coefficients are used to embed in VBs. Hiding the message in these coefficients induces minimal distortion due to JPEG’s finer quantization in this range. Actually, compression reduces the energy of all blocks. Energy thresholding gives VBs with higher energy, which can handle variations in their VCs without giving any perceptual degradation of Coverimage. Therefore VBs having more energy will protect the message even after compression attack. 4. Results and discussions

∀i , j = {0, 1, . . . , 7}, (i , j ) = 0

i =1 j =1

(10) The DC coefficient ((i , j ) = 0) is not used for calculation of energy or embedding, because any variation in DC coefficient of a block degrades the quality of the image heavily.

The proposed system has been tested with MATLAB 7.0 platform on Pentium-IV processor with 2.4 GHz and 4 GB RAM. A full fetch graphical user interface has been developed using JAVA to demonstrate embedding of text information in 512 × 512 gray scale images. More than 3500 different types of 512 × 512 gray scale images have been tested by changing the parameters r, n,

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Table 1 Number of bits saved using proposed CDCS. Number of EPR characters

Number of bits in ASCII

Number of bits in CDCS

Number of bits saved

078 253 506 584

0546 1771 3542 4088

0428 1407 2814 3244

118 364 728 844

Fig. 6. PSNR of Stego-images from the dataset.

Fig. 5. Number of bits saved with proposed CDCS mechanism.

w, QF and seed. The performance of the proposed system has been compared with various other commercially available Steganographic softwares such as COX, STOOL, DWT and SEC for the same image data set. Original Cover-images and the Stego-images obtained have been compared with respect to data hiding capacity, PSNR and IQMs. Robustness of the proposed system is tested under various intentional and unintentional attacks such as image compression, limited amounts of local and global image tampering, image resizing, low pass filtering and AWGN. Fig. 7. Effect of QF on PSNR for ‘Baboon’ image.

4.1. Data hiding capacity where, MAX is the maximum possible pixel value of the image and Initially all the images from image data set have been applied as the input to the embedding algorithm to embed 1000 ASCII characters of a text file ‘A-message.txt’. Out of 3545 images from the image data set 3025 images were eligible with VCs more than the encrypted number of bits. The proposed CDCS scheme saves lot of coding bits that are needed to be embedded in Cover-images as shown in Table 1. This saving can further be increased with increase in message length as well as increase in r as shown in Fig. 5. It can be observed that increase in data hiding capacity is the result of saving the number of bits while coding the characters with proposed CDCS mechanism. 4.2. PSNR variations The PSNR is most commonly used as a measure of perceptual quality of Stego-image. The signal in this case is the original Cover-image (C ), and the noise is the error introduced due to embedding in Stego-image (S). PSNR is used as an approximation to human perception of reconstruction quality. A higher PSNR would normally indicate that the reconstruction is of better quality. PSNR is computed in terms of Mean Squared Error (MSE) of two m × n monochrome images C and S as,

PSNR = 10 log10

MAX 2 MSE

= 20 log10

MAX MSE

(15)

MSE =

i =0 j =0 1  

mn

 C (i , j ) − S (i , j )2

m −1 n −1

Higher value of PSNR is possible by proper selection of values ˆ QF. Fig. 6 shows the plot of PSNR for all eligible Coverof w, ˆ = 1.0 images after embedding 6, 190 bits of information with w and ‘QF’ = 50. The PSNR of 2871 Stego-images (out of 3025) is more than 40 dB for embedding of 6190 bits information. Such higher values of PSNR will certainly not create any doubt in the minds of the attacker. ˆ on PSNR for various values of QF have been The effect of w tested for all images in image data set. Fig. 7 shows the result of variation in PSNR for ‘Baboon’ image. The value of PSNR increases ˆ Also the value of PSNR increases with QF for with increase in w. ˆ This is because of trade off between the image given value of w. quality and the volume of embedding at a given robustness (determined by selected QF). One can reduce QF to get maximum JPEG compression for which the Stego-image is to survive. 4.3. JPEG compression attack Digital images with hidden content may be compressed as it goes over a low bandwidth link of a wireless network. Since the embedding methodology used in our schemes is tuned to JPEG, the

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319

Table 2 Performance under JPEG Compression attack for ‘Lena’ image. QF

r

JPEG attack [bpp]

Number bits embedded

r

JPEG attack [bpp]

Number bits embedded

25 50 75

01 01 01

0.50 0.72 1.30

9120 14,360 18,790

03 03 03

0.42 0.61 1.15

3230 4630 6310

Table 3 Data hiding capacity and PSNR for proposed and SEC algorithm. Name of image file

Bits embedded by proposed algorithm

PSNR [dB]

Bits embedded by SEC [22] algorithm

PSNR [dB]

Lena Peppers Baboon Bridge Couple Boat

14,357 14,160 40,075 35,868 23,700 21,063

41.72 41.77 39.67 40.98 38.40 41.08

11,044 10,447 25,331 24,633 15,545 15,234

34.58 35.89 32.27 32.34 34.05 34.21

Table 4 Performance of the Algorithms for ‘Lena’ image under resizing attack. Interpolation method Bicubic Nearest Neighbor

Bilinear

Percentage resizing attack

Bits embedded (proposed system)

Redundancy ‘r’ (proposed system)

Bits embedded (SEC method [22])

Redundancy ‘r’ (SEC method [22])

10% 15% 20% 2% 5% 10% 2% 5% 10%

4768 4768 4768 4425 3375 2535 4425 3375 2535

3 3 3 5 7 9 5 7 9

7447 6826 6301 6301 4096 2275 2275 2155 1241

11 12 13 13 20 36 36 38 66

decoding of embedded data is perfect for all the attacks less than or equal to the given QF. Table 2 shows the number of bits embedded (with 100% recovery) at various values of QFs, under JPEG attacks for ‘Lena’ image. Quality factor plays an important role in deciding the robustness of the hidden data. More the QF at the time of embedding, more will be the non-zero DCT coefficients (that increases the embedding capacity) available for embedding. Addition of redundancy in text information bits before embedding into the images is another way to increase robustness. With redundancy the embedded information will survive for some more compression. However, increase in redundancy beyond 3 will not only reduce the embedding capacity but also gives only marginal improvement in robustness. Table 3 shows number of bits embedded (with 100% recovery) by proposed algorithm and Selectively Embedding in Coefficients (SEC) scheme [22] for QF = 25, under JPEG attacks for various Coverimages. It is observed that the data hiding capacity of the proposed algorithm is more compared to SEC algorithm. Also the value of PSNR has been improved. 4.4. Image resizing attack Image resizing is also a popular attack. In image resizing, image is shrunk to a smaller size and scaled back to its original size. During this process there is possibility of losing the information. Various interpolation methods are used for image resizing. The most popular ones are Bilinear, Bicubic and Nearest Neighbor interpolations. Table 4 shows the results of resizing attack using the said interpolation methods at QF = 25 for proposed method and SEC method [22]. It is observed that increase in resizing needs more value of r for faithful reproduction at the cost of embedding capacity. Also, survival for more resizing is possible in Bicubic interpolation as that of Nearest Neighbor and Bilinear interpolation. It is also observed that the proposed algorithm gives better results

Table 5 Performance of proposed algorithm at QF = 25 for ‘Lena’ image under local image tampering attack. Image tampering attack (%)

Bits embedded

Redundancy ‘r’

10 20 30 50

4135 2560 2560 1475

3 5 5 9

with less number of redundancy as compared to SEC algorithm. Robustness in the proposed system is achieved using inter leaving and redundancy, number of characters to be embedded will get reduced while designing the robust data embedding system for given percentage of image resizing attacks. 4.5. Image tampering attack The embedding scheme presented using proposed system is resilient to image tampered in various ways. In spite of malicious tampering of the image all the embedded bits were recovered successfully after the attack. Fig. 8(a) shows 20% tampered ‘Lena’ image. It can be observed from Table 5 that as the tampering increases, r increases for faithful reproduction of embedded information. However, this affects the overall embedding capacity. Global image tampering is as shown in Fig. 8(b). When ‘Lena’ image was embedded with redundancy, r = 7 at QF = 25, all the 1900 bits were recovered successfully from globally tampered Stego-image. 4.6. AWGN attack The method considered in this work is sustainable to mild AWGN attack. Fig. 9(a) shows ‘Lena’ image having AWGN attack with variance = 0.001 and mean = 0. With QF = 25, information

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Fig. 8. Image tampering attack: (a) ‘Lena’ image (512 × 512) tampered locally by 20%, (b) ‘Lena’ image (512 × 512) tampered globally.

Fig. 9. ‘Lena’ image with: (a) AWGN attack (variance = 0.001), (b) Gaussian low pass filter attack (a = 0.6).

of 1576 bits has successfully been embedded with said AWGN attack. Redundancy of 9 bits is necessary for faithful reproduction of embedded information. 4.7. Filter attack Low pass filter attack is very common attack in which the image is filtered using a low pass filter without noticeable difference. Fig. 9(b) shows the ‘Lena’ image with Gaussian Low Pass filter (σ = 0.6). With QF = 25, information of 2040 bits has successfully been embedded with said Gaussian low pass filter attack. Redundancy of 7 bits is necessary for faithful reproduction of embedded information. 4.8. IQM analysis The approach of attacker is based on the fact that hiding information in digital images gives rise to alterations of the image properties that introduce some form of degradation, no matter how small. These degradations can act as signatures that could be used to reveal the existence of a hidden message. For example, the Stego-image is perceptually identical but statistically different from the Cover-image. The receiver uses these statistical differences in order to decode the message. However, the very same statistical difference that is created could potentially be exploited by the attacker to determine if a given image is embedded or not. Avcibas et al. [23,24] showed in their paper that addition of a watermark or message leaves unique artifacts, which can be detected using IQMs. There are 26 different measures that are categorized into six groups as Pixel difference, Correlation, Edge, Spectral, Context and Human visual system. Avcibas et al. [23] developed a discrimina-

Fig. 10. IQM calculation.

tor for Cover images and Stego images, using an appropriate set of IQMs. To select quality metrics (features) to be used for Steganalysis, they used Analysis using Variance (ANOVA) techniques. Based on these analysis, the IQMs that are useful for Steganalysis purpose are Minkowsky Measures M 1 and M 2 , angular correlation M 4 , image fidelity M 5 , normalized cross correlation M 6 , spectral magnitude distortion M 7 , median of block spectral phase distortion M 8 and median of weighted block spectral distortion M 9 . The calculated IQMs for different embedding domain plays an important role in the process of classification of images. It has been observed that filtering an image without embedded message causes changes in the IQMs differently than the changes brought about on embedded Stego-images. The IQM scores are computed from images and their Gaussian filtered versions for selected IQMs [23] as shown in Fig. 10. Fig. 11 shows variations in IQMs for proposed, STOOL, COX and CDMA techniques. Fig. 11(a) shows the Minkowsky Measure, M 1 for different embedding domains with embedding of 5% information as that of total size of the Cover-image. It gives measure of dissimilarity. It can be observed that M 1 gives significant difference

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321

Fig. 11. Variations in IQMs for proposed, STOOL, COX and CDMA techniques.

for STOOL, COX and CDMA with respect to original image. Whereas, this difference is very negligible for the proposed system. It is well known that the major distortion in spatial domain is because of di-

rect manipulation of LSB of pixel information byte. One can easily differentiate the spatial domain images by just observing the graph of 25 different images (from Cover-image data set) as shown in

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Fig. 12. IQMs Ml, M4 and M9 at different embedding ratios.

Fig. 11(a). Similar observations can be drawn from remaining IQMs (Fig. 11(b) to Fig. 11(h)). Therefore, it is difficult for the attacker to distinguish the Stego-image using proposed algorithm and original Cover-image as compared to other Steganographic tools. The IQMs are calculated for varying percentage (1% to 15%) of embedded information. The effect of varying embedding ratios over IQMs are as shown in Fig. 12(a) to Fig. 12(c) for M 1 , M 4 and M 9 respectively for ‘Baboon’ image. It can be observed that the variations in IQMs caused due to embedding with proposed algorithm are very less as compared to variations in IQMs caused due to embedding with other Steganographic tools. The measure M 4 gives the similarity between two images. The CDMA embedding has more effect of variations with respect to embedding ratio. As percentage of information increases in CDMA the M 4 tends to be less. This indicates that increasing the embedding ratio causes more dissimilarity between two images. Variation in M 4 is less in case of STOOL and proposed DCT domain embedding as shown in Fig. 12(b). Most of the available modern Staganalysis tools uses IQMs prescribed in this paper. Because of these minimum IQMs variations the proposed system is more secured than other systems. Fig. 12(c) shows the effect of variation of embedding ratio on Spectral Measure M 9 . This measure captures the statistical distortions caused in the frequency domain. The variations in M 9 for proposed DCT domain embedding is negligible as compare to STOOL and CDMA embedding. This is due to the minimum alteration of statistics of Cover-image in frequency domain in the proposed system as compared to spatial variations in STOOL and DWT domain variations in CDMA mechanisms. 5. Conclusions The key factors to achieve robustness of the proposed scheme are a powerful coding framework that allows dynamic choice of hiding locations and embedding in robust DCT coefficients. Redundancy, interleaving, energy thresholding and randomization spreads the embedded information all over the Cover-image. This takes care of the attacks like image tampering, resizing, filtering

and AWGN. JPEG quantization reduces the possibility of corrupting the information for selected QF in compression attack. However, as the level of compression increases, the number of VCs gets reduced ˆ which in tern reduces the pay load. The embedding parameters w, QF, r and seed for the given Cover-image decides the embedding locations adaptively and becomes the integral part of the AGEK. Due to this even the transmitter does not have the explicit knowledge of locations where the information has been embedded. This increases the security level of the system. The additional advantage of the system is the feedback prompt about the suitability of the Cover-image for embedding the required number of characters. The proposed system gives better perceptual quality of Stego-image than the STOOL, COX, CDMA and SEC techniques. Increasing redundancy increases robustness but reduces payload. The embedding capacity achieved is among the best reported in the literature with added advantage of enhanced robustness and security. Proposed system gives minimum IQMs variations. Therefore the Stego-images obtained using this system are secured from the modern Steganalysis tools. Thus the proposed system can be leveraged in several exciting applications, such as image annotation, electronic patient’s report data hiding and monitoring of criminal information through their fingerprint images. Acknowledgments Authors are thankful to anonymous reviewers for their valuable suggestions that have raised eminence of the paper. References [1] F.A.P. Petitcolas, R.J. Anderson, M.G. Kuhn, Information hiding — a survey, in: Proceedings of the IEEE, Special Issue on Protection of Multimedia Content, July 1999, pp. 1062–1078. [2] S. Katzenbeisser, F.A.P. Petitcolas, Information Hiding Techniques for Steganography and Digital Watermarking, Comput. Security Ser., Artech House Books, 2000. [3] R.J. Anderson, F.A.P. Petitcolas, On the limits of steganography, IEEE J. Selected Areas Commun. Special Issue on Copyright and Privacy Protection (ISSN 07338716) (May 1998) 474–481.

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Suresh N. Mali received the B.E. degree in Electronics Engineering in 1987 and the M.E. degree in Electronics Engineering – Computer Applications in 1992 from Shivaji University, Maharashtra (India). He received his Ph.D. degree from Bharati Vidyapith (India). He is working as a teacher since 1987 and currently working as Principal, Imperial College of Engineering, JSPM Wagholi, Pune (India). His research interests are information security, data hiding, signal processing, digital multimedia communications and Steganography. Pradeep M. Patil received the B.E. degree in Electronics Engineering in 1988 from Amravati University, Amravati (India) and M.E. Electronics in 1992 from Marathwada University, Aurangabad (India). He received his Ph.D. degree in Electronics and Computer Engineering in 2004 at Swami Ramanand Teerth Marathwada University (India). He is working as a teacher since 1988 and presently he is working as Principal, Singhgad College of Engineering, Warje, Pune (India). He is a member of various professional bodies like IE, ISTE, IEEE and Fellow of IETE. He has been recognized as a Ph.D. guide by University of Pune and North Maharashtra University, Jalgaon (India). His research areas include pattern recognition, neural networks, fuzzy neural networks and power electronics. His work has been published in various national and international journals and conferences including IEEE and Elsevier. Rajesh M. Jalnekar received the B.E. degree in Electronics and Telecommunication in 1988, the M.E. degree in Electronics and Telecommunication in 1993 and Ph.D. degree in Electronics and Telecommunication from Pune University, Maharashtra (India). He has 2 years of industrial experience and 17 years of teaching experience. He is currently working as a Director, at Vishwakarma Institute of Technology, Pune (India). Research interests include information security, data hiding, digital signal processing, multimedia communications and Steganography.