A Robust Watermarking Scheme for Information Hiding K. Ramani #1, E. V. Prasad @2, S. Varadarajan *3, A. Subramanyam&4 #
CSED, SVN Engineering College, Tirupathi, India 1
[email protected]
@
Principal, JNTU College of Engineering, Kakinada, India 2
[email protected]
*
EED, SVU College of Engineering, Tirupathi, India 3
[email protected] & CSED, AITS, Rajampet, AP, India 4
[email protected]
detection of modifications to the data. By embedding watermark in commercial advertisements, an automated monitoring system can verify whether the advertisements are broadcasted as contracted or not. Indexing of video mail, indexing of movies and news items, where markers and comments can be inserted that can be used by search engine. In the case of Medical Safety it is for embedding the date and patient’s name in medical images which could be useful for safety measurement. Invisible watermarking requires a reasonable robustness against attacks such as compression, as well as little or no degradation in subjective and objective image quality. Watermarking in the frequency domain is more robust than watermarking in the spatial domain [1], because the watermark information can spread out to the entire image [2]. In the viewpoint of frequency, the high frequency area should be avoided for robustness. In order to have efficient watermarking, one should consider three main criteria: robustness against attacks such as compression, security and imperceptibility which is how much the watermarked image is similar to the original one [3]. Early watermarking schemes that were introduced in the spatial domain, where the watermark is added by modifying pixel values of the host image. Examples of such techniques are given in [3], [4] and [5]. Given its suitability to model the Human Visual System(HVS) behaviour, the Discrete Wave Transform(DWT) has gained interest among watermarking researchers, as it is witnessed by the number of algorithms following this approach that have been proposed over the last few years. There are number of steganography techniques based on least significant bits (LSB) for embedding message, however these are highly insecure, because it can be easily detectable that the data is hidden in cover object [6]. This problem can be avoidable by hiding data using Bit-Plane Complexity Segmentation (BPCS) steganography, in which image is decomposed into bit planes of sub band wavelet coefficients obtained by using Integer Wavelet Transform (IWT). At lower levels of wavelet decomposition changes made in the cover image can not identified by human naked eye. BPCS takes the
Abstract— Wavelet techniques can be successfully applied in various signal and image processing applications, namely in image de-noising, segmentation, classification, motion estimation and copy right protection. The proliferation of digitized media due to the rapid growth of networked multimedia systems has created an urgent need for copyright enforcement technologies that can protect copyright ownership of multimedia objects. Digital image watermarking is one such technology that has been developed to protect digital images from illegal manipulations. In this paper, a robust and imperceptible watermarking scheme for copy right protection is proposed. The method is based on decomposing an image using the Discrete Wavelet Transform, and then embedding locations are generated from the low frequency sub-band by using secrete sort to improve the embedding intensity. From the experimental results, the proposed scheme provides not only good quality, but also good robustness against external attacks, such as rotation, compression, cropping, noise and scaling. Keywords—Discrete Copyright Protection
Wavelet
Transform,
Watermarking,
I. INTRODUCTION With rapid growth of multimedia technology, digital watermarking for multimedia has become one of the widely used copyright protection methods. Digital watermarking is the process of conveying information by imperceptibly embedding it into the digital media. The purpose of embedding such information depends on the application and the needs of owner/user of the digital media. Main applications of watermarking include Copyright Protection, Fingerprinting, Copy protection, Image authentication, Broadcast monitoring, Indexing and Medical safety areas. The objective of Copyright Protection is to embed information about the source/owner of the digital media. Fingerprinting objective is to convey information about the recipient of digital media in order to identify every single distributed copy of the media. Watermarking can be used to control data copying the digital media in case the watermark embedded in the media indicates that media is copy protected. The objective of Image authentication is to check the authenticity of the digital media. This requires the
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advantage of human naked eye. As the level of image transformation using IWT increases maximum capacity that can be embedded decreases, peak signal to noise ratio (PSNR) decreases and bit error rate increases [6]. The data extracted from cover image also depends on the complexity pattern of the cover image. The most advanced choice from all the frequency domain method is DWT. Since, the magnitude of DWT coefficients are larger in the lowest band at each level of decomposition, it is possible to use a larger scaling factor for watermark embedding. The method in [7] shows high PSNR values between original and watermarked images even at level 3, but shows poor robustness for various geometric and signal processing attacks. Every watermark bit should be embedded in each detail (i.e. horizontal, vertical and diagonal) coefficient. Hence, the secret sorting method for watermarking is proposed. .
constant. Another technique for watermark embedding is to exploit the correlation properties of additive pseudo-random noise patterns as applied to an image [12]. A disadvantage of spatial techniques is they do not allow for the exploitation of the subsequent processing in order to increase the robustness of the watermark. Some of the frequency based watermarking techniques used the Discrete Cosine Transform (DCT) like the one suggested in 2004 by Yuehua Z. et al [13].This method is robust against compression attacks. A common problem with DCT watermarking is block based scaling of watermark image changes scaling factors block by block and results in visual discontinuity. The proposed scheme first decomposes the host image by 3-scale discrete haar wavelet transform and uses the original CA3 (low pass sub-band from third level) to get a new CA3' ( CA3 is further decomposed by 1-level and set the sub-bands CH4, CV4 and CD4 to zero, and then apply inverse 1-level DWT to retrieve the new CA3') by [9]. During the embedding phase, a random seed is used to disturb the original watermark.
II. JOO ET AL. ’S SCHEME In 2002, Joo et al. proposed a robust watermark scheme by embedding a watermark into wavelet low frequency sub-band [8]. An image with size of 512 X 512 pixels is transformed into wavelet coefficients by three level wavelet transform and extract the sub-band CA3. Then CA3 is further decomposed into four sub-bands and three sub-bands CH3, CV3 and CD3 are set to zeros. After performing inverse wavelet transform to this level CA3’ is obtained. The information embedding locations are obtained by sorting s(i,j) = | CA3 - CA3’| . In [9], the embedding operation needs a round-operation. In 2005, Lou et al. [9] improved [8] to keep S(i,j) as a secret key for watermark embedding/extraction and the efficiency of [9] is better than that in [8]. In 2004, Reddy et al. [10] calculated the percentage of embedding sub-band. It gets the weight factors from 1-level DWT of the watermark. During the extracting phase, the original image and watermark are required for the watermark extraction. This proposed method improves the watermarking scheme in [10]. It does not need the help of the weight coefficients of the original watermark and combines some aspects of [8] to generate a secret stream for watermark embedding. During the watermark embedding phase, we utilize a secret stream to execute a XOR operation with watermark and embed into the new sub-band CH3'.
A. Watermark Embedding The proposed scheme is based on the literature given in [8] and [10]. The secret sort s(i, j) and the intensity coefficients k1 and k2 are used to improve the embedding intensity. The embedding algorithm is as follows: Step 1: Decompose a host image of size m x n by 3-level DWT as shown in Fig.1 CA3
CH3
CV3
CD3
CH2 CH1
CV2
CD2
CV1
III. PROPOSED SCHEME The most straight-forward method of watermark embedding, would be to embed the watermark into the leastsignificant-bits of the cover object [11], but once the algorithm is discovered, the embedded watermark could be easily modified by an intermediate party. An improvement on basic LSB substitution would be to use a pseudo random number generator to determine the pixels to be used for embedding based on a given “seed” or key [11]. Security of the watermark would be improved as the watermark could no longer be easily viewed by intermediate parties. The algorithm however would still be vulnerable to replacing the LSB with a
CD1
Fig.1. 3-level DWT decomposition
Step 2: Sub-band CA3 is decomposed by 1-level DWT and set the sub-bands CH4, CV4 and CD4 to zero, and then apply inverse 1-level DWT to retrieve the new CA3'. Generate the secrete sort, s(i, j)= | CA3 – CA3' | using Fig.2
59
CA4
CA3
CV4
CH4
CA4
0
CD4
0
0
IV. PERFORMANCE EVALUATION The performance of the proposed method is evaluated with an 8-bit gray scale image of size 512X512 as the host image and binary image with 64X64 pixels as the watermark image.
CA3'
A. Performance Evaluation Metrics: Watermarking algorithms are usually evaluated with respect to two metrics: imperceptibility and robustness [14].
s(i,j)
Imperceptibility means the perceived quality of the host image should not be distorted by the presence of the watermark. As a measure of quality of a watermarked image, PSNR is typically used. PSNR in decibels is given by Eq. (4).
Fig. 2 Secrete sort generation
CA3 is used to generate a new CH'3 by Eq. (1). CH
3
′
= CH
3
+ {[ CA 3 ( i , j ) − CA 3 ( i + 1, j )] mod 2}......... (1)
PSNR(dB)=10log10 (2552 /MSE)
Step 3: The position of attaching the secrete information is calculated with the formula given in Eq. (2)
……………… ….. (4)
Robustness is a measure of the immunity of the watermark against attempts to remove or degrade it, intentionally or unintentionally, by different types of digital image processing [15], like image compression, guassian noise and Image cropping. The Gaussian noise is a watermark degrade attack, Joint Photographic Experts Group(JPEG) compression is a watermark removal attack and cropping is a watermark dispositioning geometrical attack. The similarity between the original watermark and the watermark extracted from the attacked image using the correlation factor, ρ is given by Eq.(5).
′ CH 3 " = CH 3 + (k1 * w(i, j ) ⊕ k 2 * s(i, j ))..............(2) where k1 and k2 are embedding intensities. The watermark image is disturbed by a random seed and embeds it into CH3 '. Step 4: An inverse wavelet transform is used to get the watermarked image B. Extraction Algorithm The proposed method is a non-blind watermarking method, hence the original host image is needed for extraction. The watermark is extracted as follows:
N
ρ = ∑ϖ i ω i ′ / i =1
N
∑ϖ i i =1
N
∑ϖ i =1
i
′ ………………..(5)
where N is the number of pixels in watermark, ω and ω' are the original and watermarks respectively. The correlation factor ρ may take values between 0 to1. In general, a correlation coefficient of about 0.75 or above is considered acceptable.
Step 1: Both of the original host image and the watermarked image are decomposed by 3-level wavelet transform. Step 2: Getting secret sort s(i,j) from the original image. Then, sub-bands CH3' and CH3 are generated by Eq. (1).
V. RESULTS The experimental results of the proposed scheme are as follows: The original host image, original watermark image, watermarked image and extracted watermark image are shown in Fig. 3.
Step 3: The embedded watermark image is retrieved by the following equation: ′ w′(i, j ) = {[CH 3 " (i, j ) − CH 3 (i, j )] / k1 ⊕ [k 2 * s (i, j )]′} ……(3) Step 4: After the Step3, a disturbed watermark is extracted and recovered with the help of the random seed. Step 5: 1-level inverse DWT is applied for the extracted watermark coefficients to get the watermark.
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watermark is recognizable. Beyond 300 it is some what difficult to identify the extracted watermark.
(a)
(b) (a)
(c)
(b)
(d) (c) (d) Fig. 6. Extraction from rotation at (b) 50 and (d) 300
Fig. 3: (a) Original image (b) Original watermark (c)Watermarked image (d)Extracted watermark
Compared to method [7] the PSNR between original image and watermarked image is better in proposed method at level 3.
D. Salt and Pepper Noise: Salt and pepper noise is added to the watermarked image with different noise densities of 0.2, 0.4, 0.6, 0.8, 0.9 and 1.0. Even for 1.0 noise density the extracted watermark is recognizable.
A. .Compression: The performance of the compressed images evaluated for 20%, 30%, 40%, 50%, 60% and 70% qualities on the watermarked image sequentially. The results of extracted are shown in Fig. 4. All extracted watermarks are recognizable. Table 1 shows the results of different qualities of compression rates. Even for 20% compression also the watermark is extractable. For more than 85 % the extracted watermark is almost similar to watermark extracted without any compression.
(a)
(b)
Fig. 7.Extraction of watermark from salt & pepper noise at noise densities (a) 0.2 and (b) 0.4
E. Cropping: The watermarked image is cropped to different block sizes, 150, 200, 250, 300, 350 and 400. Even when watermarked image cropped to a block size of 150, still extracted watermark is recognizable.
(a) (b) Fig.4: Extraction from Compression at (a) 20% and (b) 70%
B. Scaling: The watermarked image is scaled by 20%, 40%, 60%, 80%, 100% and 150%. In Fig. 5, the extracted watermarks are given, which shows that even the watermarked image is scaled by different values still extracted watermark is recognizable.
(a)
(b)
(a) (b) Fig. 5. Extraction from Resizing at (a) 20% and (b) 150%
C. Rotation: The watermarked image is scaled by 10, 50, 100 , 150 , 200 and 300. In Fig. 6, the extracted watermarks are shown for 50 and 300 rotation of watermarked image, still the extracted
(c)
(d)
Fig.8 Watermark Extraction from cropped image of (b) 20% and (d) 30% watermarked image.
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2) Scaling: Fig.11 and Fig.12 show the results of extracting watermarks from scaled watermarked images. When watermark image is scaled to 20% and 40% DCT and DWT techniques failed to extract the watermark. The proposed method is better results for all scaling values.
The PSNR and correlation values for extracted watermark are shown in Table 1, Table 2, Table 3, Table 4 & Table 5 for compression, scaling, rotation, salt pepper noise and cropping respectively. Table 1: PSNR and Correlation factor of the compressed Images compression rate PSNR(dB) Correlation factor
20%
30%
40%
50%
60%
51.3
51.45
51.57
51.59
51.61
52.1
0.903
0.9085
0.912
0.9233
0.9269
0.9464
3) Rotation: Fig 13 and 14 show the results of extracted watermarks from rotated watermarked images. DCT method is having better PSNR and correlation factor values for different angles of rotation compared to DWT and DWT with secrete sorting methods. But the proposed method is having better results than DWT method.
70%
4) Cropping: As shown Fig.15 and Fig. 16, the proposed method suffers much less from cropping attack compared DCT and DWT methods. But, DWT is having better performance than DCT for this attack.
Table 2: PSNR and Correlation Factor of the scaled Images Scaling
20%
40%
60%
80%
100%
150%
PSNR (dB) Correlation factor
41.04
43.51
44.17
45.18
48.92
49.98
0.4324
0.4625
0.5062
0.6904
0.8007
0.8123
5) Salt and Pepper Noise: The robustness against salt and pepper noise is tested for all three methods. Again the proposed method is having good correlation value for different noise densities. However the difference in PSNR values is less compared to other two methods at 0.6, 0.8 and 1.0 noise density values.
Table 3: PSNR and Correlation Factor of the rotated Images 10
50
100
150
200
300
PSNR (dB)
49.73
45.11
42.34
41.21
38.83
34.60
Correlation factor
0. 81
0. 77
0. 71
0.70
0.69
0. 68
Rotation angle (degrees)
Table 4: PSNR and Correlation Factor of the Salt & Pepper Noised images Noise density
0.2
0.4
0.6
0.8
0.9
1.0
PSNR (dB)
51.85
50.94
50.15
48.21
47.89
45.60
Correlation factor
0.9178
0.9156
0.9145
0.8932
0.8670
0.8126
Table 5: PSNR and Correlation Factor of Cropped images Cropped Block Size
400
350
300
250
200
150
PSNR (dB)
50.82
51.36
51.68
51.85
51.97
52.66
Correlation factor
0.7108
0.7156
0.7845
0.8033
0.8349
0.8696
Fig. 9 Correlation factor of different watermarking methods based on compression attack.
F. Comparison of the proposed with various other techniques: 1) JPEG Compression: By observing Fig. 9 and Fig. 10 the robustness against the JPEG compression is higher for DCT embedding algorithm [16] only for quality factor greater than 50. But, the proposed method is having better PSNR and Correlation factor values for lower quality factors of 20, 30, 40 and 50 compared to DCT and DWT [7] methods. Fig. 10 PSNR of different watermarking methods based on compression attack.
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Fig. 11 Correlation factor of different watermarking scaling attacks
Fig. 14 Correlation factor of different watermarking methods based on rotation attack
methods based on
Fig. 15 PSNR of different watermarking methods based on cropping attack Fig. 12 PSNR of different watermarking methods based on scaling attack
Fig. 16 Correlation factor of different watermarking methods based on cropping attack
Fig. 13 PSNR of different watermarking methods based on rotation attack
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REFERENCES [1] W. Bender, D. Grahul, N. Morimoto, and A. Lu, “Techniques for Data Hiding”, IBM Systems Journal, vol. 35, no. 3-4, 1996, IBM, USA, pp. 313-336. [2] I. J. Cox, J. Killian, T. Legthton, and T. Shamoon, “Secure Spread Spectrum for Multimedia,” IEEE Trans. On Image Processing, vol. 6, no.12, 1997, pp. 1673-1687. [3] R. G. Schyndel, A. Z. Tirkel, N. R. Mee, and C. F. Osborne, “A digital watermark,” IEEE Int. Conf. Image Processing. Vol.2, pp. 86-90,Texas, U.S.A, November, 1994. [4] R. B. Wolfgang, and E. J. Delp, “A watermark for digital images,” School of Electrical Engineering, Purdue University, USA, Tech. Rep., 1995. [5] S. P. Maity, and M. K. Kundur, “Robust and blind spatial watermarking in digital image,” Dept. of Electronics and Telecomm., India, Tech. Rep., 2001. [6] K. Ramani, Dr. E. V. Prasad, and Dr. S. Varadarajan, “Steganography Using BPCS to the Integer Wavelet Transformed Image”, International Journal of Computer Science and Network Security, Vol.7 No 7, July 2007, pp. 293-301. [7] Ramani, K, Prasad, E.V and Varadarajan, S, “DWT Based Watermarking For Biometric Data”, Indian Journal of Information Science and Technology, Vol. 3, November 2007, pp. 35-41. [8] S. Joo, Y. Suh, J. Shin, H. Kikuchi, and S. J. Cho., “A new robust watermark embedding into wavelet DC components,” ETRI Journal, 24, 2002, pp. 401-404. [9] Ejima. M and A. Myazaki,2001. “On the evaluation of performance of digital watermarking in the frequency domain”, in proc. Of the IEEE Int. Conf. on Image Processing. 2:546-549. [10] Voloshynovskiy. S., S. Pereira and T. Pun. 2001. “Attacks on Digital watermarks: classification, Estimation-Based attacks and Benchmarks”, Comm, Magazine.,39(8):118-126. [11] N.F. Johnson, S.C. Katezenbeisser, “A Survey of Steganographic Techniques” in Information Techniques for Steganography and Digital Watermarking, S.C. Katzenbeisser et al., Eds. Northwood, MA: Artec House, Dec. 1999, pp 43-75 [12] G. Langelaar, I. Setyawan, R.L. Lagendijk, “Watermarking Digital Image and Video Data”, in IEEE Signal Processing Magazine, Vol 17, pp 20-43, September 2000. [13] Z. Yuehua, C. Guixian and D. Yunhai, “An image watermarking algorithm based on discrete cosine transform block classifying,” ACM Int. Conf. October, 2004, pp. 234-235, Shanghai, P. R. China, 2004. [14] J. L. Liu, D.C. Lou, M. C. Chang, & H. K. Tso, “A robust watermarking scheme using self-reference image,” Computer Standards & Interfaces, 28, 2005, pp. 356-367. [15] A. A. Reddy, and B. N. Chatterji, “ A new wavelet based logowatermarking scheme,” Pattern Recognition Letters, 26, 2005, pp. 1019-1027 [16] A. Piva, M. Barni, F. Bartolini, and V. Cappellinni, “DCT- watermark recovering without restoring to uncorrupted original image,” in International Conference on Image Processing, Vol .III, pp.520523,1997.
Fig.17 Correlation factor of different watermarking methods based on salt and pepper noise attack
Fig.18 PSNR of different watermarking methods based on salt and pepper noise attack
VI. CONCLUSION The spatial domain watermarking scheme is vulnerable to image processing attacks, hence, the proposed scheme modifies the original image in transform domain and embeds a watermark in the difference values to overcome the weak robustness problem in spatial domain, and methods in [6] and [7]. The time taken by the proposed scheme for embedding and extraction of watermarking is several times less than using Shoemaker’s method for embedding and extraction of JPEG images and also it is more robust. The proposed method has simple steps for watermarking and extraction and it is resistant against many image processing attacks. But, the proposed method is non-blind, which requires the original image for extraction of the watermark. The proposed method is compared with DCT [16] and DWT [7] methods. For JPEG compression and rotation attacks the DCT is showing better performance compared to proposed and DWT method. For cropping, scaling and noise affects the proposed method is more robust than other two methods. Further research can be done in extending this work to combined Discrete Cosine Transform-Discrete Wavelet Transform, Dual Tree Complex Wavelet Transform and Combined Discrete Cosine Transform-Dual Tree Complex Wavelet Transform methods.
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