Zero-Watermarking in Transform Domain and Quadtree Decomposition for Under Water Images Captured by Robot

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International International Conference Conference on on Robotics Robotics and and Smart Smart Manufacturing Manufacturing (RoSMa2018) (RoSMa2018)

Zero-Watermarking in Transform Domain and Quadtree Decomposition for Under Water Images Captured by Robot 1 1 Ayesha Ayesha Shaik Shaik1 ,, V. V. Masilamani Masilamani1 Department Department of of Computer Computer Science Science and and Engineering, Engineering, Indian Indian Institute Institute of of Information Information Technology Technology Design Design and and Manufacturing, Manufacturing, Kancheepuram, Kancheepuram, Chennai-600127, Chennai-600127, India India

Abstract Abstract To To protect protect the the copyrights copyrights of of the the digital digital multimedia multimedia in in the the information information security security domain, domain, digital digital watermarking watermarking is is still still aa solution. solution. But recently, most of the researchers focused on the zero-watermarking technology where the original image is But recently, most of the researchers focused on the zero-watermarking technology where the original image is undisturbed undisturbed by by watermark embedding. embedding. In watermark In mission-critical mission-critical surveillance surveillance applications applications (intrusion (intrusion detection detection and and disaster disaster management management scenarios) scenarios) where where the the mobile mobile robots robots are are used, used, it it will will be be aa good good idea idea to to watermark watermark the the images images captured captured by by the the robots robots to to prevent prevent the the copyright copyright ininfringement. If we watermark robot captured images (RCI) visual quality degradation of the image will be of main concern. fringement. If we watermark robot captured images (RCI) visual quality degradation of the image will be of main concern. So So aa zero-watermarking scheme scheme using using non non sub-sampled sub-sampled contourlet contourlet transform transform and and quadtree quadtree decomposition decomposition is is proposed proposed for for RCI RCI in in this this zero-watermarking article. article. The The approximated approximated sub-band sub-band obtained obtained by by contourlet contourlet transform transform is is undergone undergone quadtree quadtree decomposition, decomposition, to to split split the the sub-band sub-band into into blocks blocks of of different different sizes. sizes. The The sparse sparse features features of of these these blocks blocks and and the the statistical statistical distribution distribution of of the the sub-band sub-band coefficients coefficients are are exexploited to to generate generate aa zero-watermark zero-watermark which which is is used used for for ownership ownership authentication. authentication. The The experimental experimental results results show show that that the the proposed proposed ploited method method outperforms outperforms the the existing existing scheme, scheme, and and resists resists most most of of the the geometrical geometrical attacks attacks and and the the signal signal processing processing attacks attacks effectively. effectively.

© 2018 The Authors. Published by Elsevier Ltd.  2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. cc 2018  This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). This is an open access article under the scientific CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the committee of the International Conference on Robotics and Smart Manufacturing. Keywords: Contourlet, Keywords: Contourlet, Zero-watermarking, Zero-watermarking, Ownership Ownership authentication, authentication, Quadtree Quadtree decomposition, decomposition, Robust, Robust, Non Non sub-sampled sub-sampled contourlet contourlet transform transform

1. Introduction 1. Introduction Mobile Mobile robots robots play play an an important important role role in in mission-critical mission-critical surveillance surveillance applications applications [4, [4, 9] 9] such such as as intrusion intrusion detection detection or disaster relief applications. Instead of using a fixed image camera which is mounted in a predefined or disaster relief applications. Instead of using a fixed image camera which is mounted in a predefined direction, direction, 0 mobile mobile robot robot (the (the robot robot which which moves) moves) which which can can be be fixed fixed on on aa rotatable rotatable axis axis will will be be advantageous advantageous as as it it provides provides 360 3600 visual visual coverage. coverage. These These mobile mobile robots robots play play an an important important role role in in capturing capturing the the unprecedented unprecedented footage footage of of marine marine life life [10], stunning images of African lions [16] and dangerous environments where humans cannot be entered. [10], stunning images of African lions [16] and dangerous environments where humans cannot be entered. It It will will be be beneficial beneficial if if the the images images captured captured by by these these robots robots are are watermarked watermarked to to avoid avoid copyright copyright infringement. infringement. If If these these RCIs RCIs ∗ ∗

Corresponding Corresponding author. author. Tel.: Tel.: +91 +91 91760 91760 10587 10587 E-mail address: [email protected] E-mail address: [email protected]

1877-0509   2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. cc 2018 1877-0509 1877-0509 © 2018 The Authors. by Elsevier Ltd.(http://creativecommons.org/licenses/by-nc-nd/3.0/). This is an open access article under Published the CC CC BY-NC-ND BY-NC-ND license This isisan article under the license (http://creativecommons.org/licenses/by-nc-nd/3.0/). This anopen openaccess access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Robotics and Smart Manufacturing. 10.1016/j.procs.2018.07.047

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are watermarked the visual quality of the watermarked images will be degraded in highly sensitive environments as discussed above. Ideally, there should not be any visual deviation in the quality of the original image and watermarked image which is known as imperceptibility. To authenticate the copyrights of the owner, the information needs to be embedded in a way that the watermarking scheme can resist the attacks. So to trade-off between imperceptibility and robustness mostly human visual system (HVS) are used [2, 11, 5]. So the researchers have developed zerowatermarking schemes where the watermark is not embedded and still we can protect the copyrights of the owner. The statistical characteristics of the original data can be used to construct the zero-watermark. Zero-watermarking schemes are most popular as the authentication of the owner can be done without disturbing the digital data. So, a zerowatermarking scheme using nonsub-sampled contourlet transform and quadtree decomposition for underwater images is proposed in this article. Here, the statistical distribution of the contourlet coefficients and the sparse features of the quadtree decomposition are exploited to generate the zero-watermark. The rest of this article is organized as follows. Section 2 provides a brief literature review of available zero-watermarking schemes and provides the description of nonsub-sampled contourlet transform, quadtree decomposition, and NIG distribution. Section 3 presents the proposed zero-watermarking scheme in detail. Section 4 discusses experimental results. Finally, conclusion is given in section 5.

Fig. 1: Wavelet

Fig. 2: Contourlet

2. Literature Survey and Preliminaries The image zero-watermarking algorithms in spatial domain where zero-watermarks are constructed using third order and fourth order cumulants of higher-order components [18] and the scale-invariant feature transform (SIFT) features located in larger scale space and their corresponding descriptors are reported in [27]. The image zerowatermarking algorithm using the AC coefficients of discrete cosine transform (DCT) has been reported in [19]. The zero-watermarking scheme using the discrete wavelet transform (DWT) where zero-watermark is constructed using low-frequency coefficients ha been discussed in [1]. A few moments used in image processing include Hus moments and Zernike moments. Using Hus moments a zero-watermarking scheme has been reported in [26] where zero-watermark is generated using the seven invariance moments those can resist RST (Rotation, Scale, and Translation) attacks and another zero-watermarking scheme that use Zernike moments amplitude that has properties of rotational and reversal invariance to construct zero-watermark has been reported in [20]. The K-L transform was used in the zero-watermarking scheme for zero-watermark construction using the principal components of each image block has been discussed in [7]. The image zero-watermarking scheme based on singular value decomposition (SVD) where zero-watermark is generated using singular values of the images has been presented in [23]. Existing zero-watermarking schemes have been discussed in [14] for audio samples. Neural networks based zero watermarking scheme in spatial domain has been reported in [13]. A DWT zero-watermarking scheme with chaotic modulation has been presented in [6]. A robust linear prediction cepstrum coefficients based zero-watermarking scheme for audio samples has been reported in [17]. A zero-watermarking scheme which uses energy comparison of audio samples has been discussed in [25]. A zero watermarking scheme for audio samples using discrete wavelet and discrete cosine coefficients in transform domain has been discussed in [24]. A zero-watermark algorithm based on the contourlet transform for digital videos has been reported in [22]. The zero-watermark is constructed with the selected significantframes that have the highest entropy. A digital video zero-watermarking algorithm using DWT and DCT log-polar coordinates based on log-polar mapping (LPM) has been reported in [12]. This technique tolerates noise, rotation, filtering, and compression attacks. Another zero-watermarking algorithm based on text detection for digital videos has



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been presented in [21]. In [23] a study on zero-watermarking schemes has been reported where the existing schemes have been analyzed and experimented. 2.1. Non Sub-Sampled Contourlet Transform Contourlet transform is an image representation method which is a variant of the wavelet transform. The wavelet transform produces a lot of redundancy and poor precision with the large-scale square approach to approximate a curve. By approximating the curve with the small scale square approach, may improve the accuracy, which also increases the data volume as shown in Fig. 1. Contourlet transform uses the different size long rectangles to flexibly approximate the curve which improves the accuracy and will not generate a large volume of data as shown in Fig. 2. The contourlet transform is considered to be an efficient method to represent the two-dimensional images with smooth contours as it outperforms wavelet transform which fails to identify the smoothness of the contour. Contourlet trans-

. . . . (2,2)

Bandpass directional subbands

Bandpass directional subbands

(2,2)

Image

Bandpass directional subbands

Fig. 3: Contourlet

form offers better sparseness with a higher degree of directionality. A shift invariant version of contourlet known as non sub-sampled contourlet transform (NSCT) is a transform which provides flexible multiscale, multidirectional expansion [3]. The working procedure of the NSCT can be classified into two processes: one is the nonsubsampled pyramids (NSP) and the nonsubsampled directional filter bank (NSDFB). The NSP executes multiscale decomposition by dividing the image into a low-frequency subband and a high-frequency subband at each level. The NSDFB carries out decomposition of directional information which will generate k + 1 subband images that consists of one low-frequency image and k high-frequency images, given the decomposition level k. The schematic structural diagram of the NSCT is shown in Fig. 3. NSCT is very much helpful in image enhancement operations [3] to distinguish the noise from the weak edges in the frequency domain as both (noise and weak edges) will have low-magnitude. An example of contourlet decomposition for an underwater image captured by Isis ROV is shown in Fig. 4. 2.2. Quadtree Decomposition Quadtree decomposition divides an image of size N × N pixels into four blocks of equal size. Then each block will be tested whether blocks have the property of homogeneity. If a block has homogeneous regions, then it is not divided further. If the block does not have the homogeneity property then the block is subdivided into four equal sized blocks, and those blocks will be tested again for homogeneity property and so on. In this manner, this decomposition process is iteratively progressed until each block meets the homogeneity property. The resultant image obtained by

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(i)

(iii)

(ii)

(iv)

Fig. 4: Image decomposition with NSCT. (i) shows original image (predatory monkfish), images in (ii) and (iii) shows decomposition of the cover image with different decomposition levels and image in (iv) shows reconstructed image

(a)

(b)

(c)

(e)

(d)

(f)

Fig. 5: Quadtree decomposition of an underwater image captured by the robot in North East Atlantic Ocean. In the first row, the first image is original image and other two images are the quadtree decompositions with 0.27 and 0.35 thresholds. The second row shows the decompositions of the first image with thresholds 0.48, 0.78 and 0.91. The threshold is specified as a value between 0 and 1. Here the blocks are split if the maximum value of the block elements minus the minimum value of the block elements is greater than the threshold. It can be noted that if the threshold is increasing the sparseness is improving.

the quadtree decomposition might have blocks of several different sizes as shown in the Fig. 5. From this figure, one can see that the threshold also plays a vital role in block decomposition as it affects the sparsity of the decomposed matrix by the threshold set for block splitting. 2.3. Normal Inverse Gaussian Distribution To generate the watermark for the curvelet transformed coefficients, a mixed distribution known as normal inverse Gaussian (NIG) distribution is used which is a mixture of normal and inverse Gaussian (IG) distribution. From [8], the NIG distribution can be represented as P = µ + βR +

√ RQ

(1)

where Q is N(0, 1); R is IG (γ, δ) with mean = γδ and variance = γδ3 . If r follows itself an IG distribution and the conditional distribution p given r is N(µ + βr, r) then the resulting mixed distribution is the normal inverse gaussian distribution(α, β, µ, δ) denoted as NIG(α, β, µ, δ) with the parameters scale, location, skewness and kurtosis, where  α = γ2 + β2 . The IG probability density function is given by [15] PIG (r) =

δ expδ γ r √ 2π

−3 2

−1

exp 2

2

( δr +γ2 r)

The NIG probability density function can be defined as follows [8]: αδ expδ γ+β(p−µ) K1 (α δ2 + (p − µ)2 ) PNIG (p) =  π ( δ2 + (p − µ)2 )

(2)

(3)



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where Kg (p) is modified bessel function of second kind with index g. α is shape parameter and β, δ and µ are skewness, scale and location parameters respectively, where δ > 0 , −∞ < µ < ∞ and 0 ≤ mod β < α. For symmetric and zero-mean distribution µ = β = 0. statistical distribution

watermark generation

quadtree decomposition

NSCT

modulation zero watermark

Owner authentication key

scrambling

Unique key

Fig. 6: Zero-watermark construction

3. Proposed Zero-Watermarking Scheme 3.1. Zero-Watermark Generation

The proposed zero-watermarking algorithm in NSCT domain and quad tree decomposition is given in Fig. 6. Zero-watermark is constructed using the statistical distribution of the contourlet coefficients. Let us assume that the coefficients selected to generate watermark are, q = (qi ), where qi = (qi1 , qi2 , . . . , qik ) which are i.i.d by NIG distibution. Then watermark bit wi = 0, is generated if PNIG (0/qi1 , qi2 , . . . , qik ) > PNIG (1/qi1 , qi2 , . . . , qik ); else watermark bit wi = 1, is generated. So the generated watermark is w = wi . Now to produce more randomness in the watermark, the sparse matrix (B) of the blocks generated by quadtree decomposition is used. Now the randomized zero-watermark is constructed as GwB = (w) ⊕ B. Here, w is adjusted to the size of B for the computational comfortness and also for randomness which helps to build secure zero-watermark. The copyright of the owner E (2D binary image) is scrambled with GwB to produce a unique 2D sequence U ID = E ⊕ GwB which is exploited during copyright verification when owners authentication is at risk. statistical distribution

NSCT

watermark generation

quadtree decomposition

modulation zero watermark

scrambling

Owner authentication key

Fig. 7: Zero-watermark extraction

3.2. Zero-Watermark Extraction The zero-watermark extraction algorithm is shown in Fig. 7 and is extracted as following.

Unique key

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Author name / Procedia Computer Science 00 (2018) 000–000

PNIG (qi1 , qi2 , . . . , qik /1)

1 P(1) P(0) ≷ PNIG (qi1 , qi2 , . . . , qik /0) P(qi1 , qi2 , . . . , qik ) 0 P(qi1 , qi2 , . . . , qik )

(4)

From equation (4), if PNIG (qi j /1) > PNIG (qi j /0); 1 ≤ i ≤ x, 1 ≤ j ≤ y, x < L, y < N, ith watermark bit wi is extracted as bit 1. Otherwise ith watermark bit wi is extracted as 0. Then the equation can be simplified as n  i=1

 K1 (α δ2 + ln(  K1 (α δ2 +

2 qi j 1+sign(qi j )η ) 2 qi j 1−sign(qi j )η )

1

)≷

n 

0 i=1

ln(

1 − sign(qi j )η 1 ) ≷ Th 1 + sign(qi j )η 0

(5)

  1−sign(q )η 1−sign(q )η where T h = ni=1 ln( 1+sign(qii jj )η is the threshold for watermark bit extraction. If ni=1 ln( 1+sign(qii jj )η ) > T h then wiex is extracted as bit 0; else wiex is extracted as bit 1. So the extracted watermark is we = wiex . Now the sparse matrix (Battack ) of the blocks generated by quadtree decomposition is used to generate a randomized extracted watermark. Now the randomized zero-watermark is computed as GwBex = we ⊕ Battack . Here, we is adjusted to the size of Battack for the computational comfortness and also for randomness which helps to extract secure zero-watermark. then GwBex is scrambled with unique 2D sequence U ID generated in zero-watermark construction phase, Eex = U ID ⊕ GwBex . Now to verify the ownership, the correlation betwen E and Eex are calculated. If the correlation is higher than a predefined threshold then the ownership is authenticated; otherwise the owner is not authenticated. 4. Experimental Analysis The validation of the proposed method is done with the images in dataset [10]. The experimental analysis has also been carried out on the images in SIPI dataset. In order to verify the robustness of the proposed scheme, the correlation values between the generated and extracted watermarks denoted as NC(E,Eex ), correlation between the sparse matrices obtained from quadtree decomposition of the images without noise and with noise denoted as CS(B,Battack ), and bit error rate values between the generated and extracted watermarks denoted as BER(E,Eex ) have been considered and tabulated in Table 1. In the table, AF represents average filtering attack, SPN represents salt and pepper noise attack, GN(0,x) represents Gaussian noise attack with zero mean and variance x, and MF represents median filtering attack. For AF and MF attacks, 3x3, 5x5 and 7x7 window sizes are considered. In SPN attack, we have examined the proposed scheme for 0.001, 0.01, and 0.05 intensities. In this table 1, one can see that the NC and CS values are decreasing with the increase in strength of the attack, and the BER values are increasing with the increase in strength of the attack. This is due to the fact that as the amount of attack increases, the visual quality of the original image degrades, which affects the sparse matrix of the quadtree decomposition during zero-watermark extraction. In order to analyse the performance of the proposed method it has been verified with the images from SIPI dataset images as well. The affects of salt and pepper noise attack for the quadtree decomposition on the images without noise and with noise is shown in Table 2 for 0.001, 0.01 and 0.1 intensities. One can note that the sparsity of the quadtree decomposition is varying for the image with more noise. The time taken for the contourlet transform is 0.201705 secs, quadtree decomposition is 0.035248 secs, zerowatermark generation is 0.209237 secs, and zero-watermark extraction is 0.256594 secs for the proposed method. The time taken for the discrete wavelet transform is 0.771617 secs, discrete cosine transform is 0.013779 secs, zerowatermark generation is 0.117477 secs, and zero-watermark extraction is 0.835547 secs for the existing method [24]. 5. Conclusion A zero-watermarking scheme for underwater images in the transform domain is proposed in this article. The statistical distribution of the non sub-sampled contourlet transform coefficients and the sparsity matrix of quadtree decomposition are used to generate the zero-watermark. The novelty of this scheme is the use of the sparseness of quadtree decomposition for zero-watermark construction. The proposed method is validated experimentally by using the standard dataset images and exhibits better performance compared to the competent zero-watermarking technique.



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Table 1: Correlation and bit error rate achieved for the proposed method for a set of images against a set of attacks with various strengths of the attack

Img Img1 Img2 Img3

NC and BER NC CS BER NC CS BER NC CS BER

3x3 1 1 0 0.997 0.997 0.0007 1 1 0

AF 5x5 0.991 0.965 0.002 0.997 0.996 0.0007 1 1 0

7x7 0.972 0.892 0.007 0.985 0.983 0.0037 0.967 0.948 0.0088

0.001 0.996 0.993 0.001 1 1 0 0.994 0.983 0.0007

SPN 0.005 0.943 0.86 0.007 0.985 0.866 0.003 0.97 0.933 0.003

Attack 0.007 0.941 0.856 0.008 0.978 0.856 0.005 0.964 0.924 0.005

(0,1) 1 1 0 0.97 0.007 0.03 0.942 0.178 0.014

GN

(0,2) 0.97 0.927 0.0073 0.94 0.0073 0.033 0.874 0.178 0.033

3x3 1 1 0 0.996 0.996 0.0007 0.996 0.976 0.0007

MF 5x5 0.996 0.979 0.001 0.984 0.98 0.003 0.961 0.964 0.009

7x7 0.984 0.975 0.005 0.982 0.975 0.0044 0.952 0.956 0.011

Table 2: Quatree decomposition of the images without noise and with different strengths of salt and pepper noise

Images

No attack Image

QD

0.001

QD

0.01

SPN

QD

0.05

fQD

Monkfish

Cameraman

Peppers Table 3: Correlation and bit error rate achieved for the proposed method for a set of images against a set of attacks with various strengths of the attack

Scheme Method in [24] Proposed Method

NC and BER NC BER NC BER

3x3 0.9257 0.001 0.996 0.001

AF 5x5 0.863 0.002 0.993 0.001

7x7 0.848 0.007 0.986 0.004

0.001 0.892 0.027 0.989 0.004

SPN 0.005 0.858 0.044 0.943 0.01

Attack 0.007 0.824 0.09 0.924 0.021

GN (0,1) (0,2) 0.861 0.764 0.06 0.007 0.952 0.93 0.002 0.007

3x3 0.858 0.05 0.99 0.003

MF 5x5 0.838 0.06 0.983 0.004

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