ENTROPY-BASED IMAGE WATERMARKING USING DWT AND HVS

SETIT 2005 3 rd International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 27-31, 2005 TUNISIA ENTROPY-BASED IMAGE WATERMARKING USING DWT AND HVS Shiva Zaboli *, Arash Tabibiazar * and Reza Safabakhsh * * Computer Engineering & IT Department, Amirkabir University of Technology (Tehran Polytechnic) sh_zaboli@persialogic.com a_tabibiazar@persialogic.com safa@ce.aut.ac.ir Abstract: There are a lot of wavelet-based approaches in digital image watermarking literature. In these approaches, the main issue is selection method of wavelet sub-bands coefficients and embedding algorithm. In this approach, an entropy-based method is proposed for non-blind watermarking of still gray level images using discrete wavelet transform. In our approach, we have also used the Discrete Wavelet Transform (DWT) feature and Human Visual System (HVS) characteristic in embedding phase of watermarking algorithm. In comparison with well-known DWT based methods and against the existence attacks in literature, it shows better performance. With simple modifications the method can be used for color images and in real time systems. Key words: DWT, Entropy, HVS, Watermarking 1. Introduction Currently, virtually all multimedia production and distribution is digital. The advantages of digital media for creation, processing and distribution of productions are well known: easy modification and possibility of software processing rather than the more expensive hardware alternative. Maybe the most important advantage is the possibility of unlimited copying of digital data without any loss of quality. This latter advantage is not desirable at all to the media producers and content providers. In fact, it is perceived as a maor threat, because it may cause them considerable financial loss. Digital watermarks have been proposed as a way to tackle this problem. This digital signature could discourage copyright violation, and may help determine the authenticity and ownership of an image. Ideal characteristics of a digital watermark have been stated (Fu 1998). Many watermarking methods have been proposed in the literature. (Cox & al., 1997) noted that in order for a watermark to be robust to attacks, it must be placed in perceptually significant areas of the image. The watermark was based on 00 random samples of a N(0,1) distribution. These samples were added to the 00 largest DCT coefficients of the original image. (Xia & al., 1997) proposed a watermarking scheme based on the Discrete Wavelet Transform. The watermark, modeled as Gaussian noise, was added to the middle and high frequency bands of the image. The decoding process involved taking the DWT of a potentially marked image. (Bartolini & al., 1998) generated a watermarked image from DCT coefficients. Then spatial masking was performed on the new image to hide the watermark. (Kundur & al., 1997) embedded the watermark in the wavelet domain. The strength of the watermark was determined by the contrast sensitivity of the original image. Both techniques showed resistance to common signal processing operations. (Delaigle & al., 1997) proposed a unique watermarking scheme based on the Human Visual System. Binary m-sequences were generated and then modulated on a random carrier. This image served as the watermark, and then it was masked based upon the contrast between the original signal and the modulated image. Their technique was robust to additive noise, JPEG coding, and rescanning. (Bas & al., 1998) introduced a watermarking system using fractal codes. A collage map was composed of 8x8 blocks of the original image and of the image s DCT. The watermark was added to the collage map to produce a marked image. Results showed that fractal coding in the DCT domain performed better than coding in the spatial domain. Although different transforms have been used in digital watermarking

schemes reported in the literature, there is no common framework for multi-resolution digital watermarking of both images and video. In this paper, we propose a new approach to digital watermarking of images based on the two-dimensional discrete wavelet transform. This method is motivated by the fact that most network-based images are in compressed form and that wavelets play an important role in the upcoming compression standards such as JPEG2000. We experimentally show that a watermark signal can be embedded in an entropy-based selection of wavelet coefficients without any impact on the image visual fidelity. The watermark added at a lower resolution is itself watermarked at a higher resolution. The hierarchical nature of the wavelet representation allows detection of watermarks at all resolutions. Detection of lower resolution watermarks reduces computational complexity, as fewer frequency bands are involved. The multi-resolutional property makes the proposed watermarking scheme robust to image/video down sampling operation by a power of two in either space or time. We also test the proposed watermarking scheme against common distortions introduced by compression and geometrical attack. Experiments show that for both cases, the corresponding watermark can still be correctly identified at each possible resolution in the wavelet domain. 2. Watermarking in the wavelet domain Presently, the most advanced choice among all the frequency domain methods is probably the DWT. The DWT is a hierarchical transform, which offers the possibility of analyzing a signal at different resolutions or levels ( λ ). Such multi-resolution analysis gives a frequency domain representation as a function of time; i.e., both time/space and frequency localization exists. In order to achieve this, the analyzing functions must be localized in time. For watermarking, we need to select an appropriate wavelet or basis. Most of the basis developments have taken place in the context of image compression; and fortunately, watermarking and compression have many things in common. On the other hand, we certainly need to choose a basis that offers compact support. The smaller the support of the wavelet, the more energy the transform compacts in the high frequency sub-bands. Also we are restricted to a class of either orthogonal or bi-orthogonal wavelets. Filter regularity and symmetry and a smooth wavelet function are effective in the reconstructed image quality. In addition, we need a reasonably good HVS model for the selected basis. Finally, we ideally prefer shift invariance in watermarking in order to handle geometric attacks (Serdean & al., 2002). For this work, we selected the Antonini 7.9 wavelet, which is one of the best wavelets available for image compression. This wavelet is widely used in image compression algorithms. A maor advantage of the DWT lies in the fact that it performs an analysis similar to that of the HVS. The HVS splits an image into several frequency bands and processes each band independently. Finally, more general advantages of the DWT are: It is not a block based transform, and so the annoying blocking artifacts associated with the DCT are absent. Its multi-resolution property offers more degrees of freedom compared with the DCT. Lower computational cost than the FFT or DCT: O(n) instead of O(nlog(n)), where n is the order of the transform input vector. Better energy compaction than both the FFT and DCT in the sense that it is closer to the optimal Karhunen-Love transform. 3. The proposed watermarking scheme The proposed wavelet-based watermark embedding and extraction scheme (MYWA) is shown in Figure 1. DWT Entropy XOR PN Sequence Initial Seed Embedding num of selected coefs Selected Coefs I + α.s. ω ~ ( I IDWT HVS I) α. S Attack Extraction DWT Extract Watermak Figure 1. The proposed watermarking scheme (MYWA) For embedding the original watermark, we select a set of high value coefficients in each level sub-band ( λ, ) by adusting the initialized chiprate variable. The value of this variable is defined in an adaptive way by multiplication of entropy of the corresponding detail with an initial constant value that is set by the owner. The watermark is embedded using amplitude modulation as follows. Suppose Q( λ, ) I S =. Q min mean ( I ) I that,, where is the selected coefficient in each sub-band, and the direction at each level of decomposition ( λ ). Also,. denotes the absolute value, Q( λ, ) is a rough measure of the visibility which is extracted from human visual system (HVS) presented by Watson et al. for bi-orthogonal

Qmin wavelet basis 7.9 (Ambroze & al., 2001), and is the minimum value of quantization matrix (Table 1). We will use equation [1] for watermarking the decomposed image coefficients: ~ I = I + α. S. w And will use equation [2] for LL of third level: ~ 1 I = I +. α. Q min. S. w 2 (1) (2) Based on the HVS model and experimental methods, it is shown that values of S greater than 24 will decrease the visibility of watermarked image, so all values greater than 24 for S are set to 24 (Ambroze & al., 2001). In our method, embedding is applied to HL, LH, and HH sub-bands at all levels of decomposition and to LL detail at the third level. In the watermark extraction, we first evaluate a similarity measure and then compare it with a threshold (chosen as T=6) to decide if the watermarks match, see equation [3] (Cox & al., 1997). Cox and Miller showed that similarity follows the standard normal distribution with unit variance since W and V are uncorrelated (Cox & al., 1997). Setting the threshold (T = 6) makes the probability of wrong watermark detection very small (Cox & al., 1997). Sim ( W, Wˆ ) = W. Wˆ / Wˆ Table 1. Antonini 7.9 Quantization Matrix (Watson & al., 1996) Orientation Level r=32pixel/degree 1 2 3 LL 14.049 11.6 11.363 HL 23.028 14.685 12.707 HH 58.756 28.408 19.54 LH 23.028 14.685 12.707 2 (3) The original watermark is extracted from a binary logo image, which is scrambled by a well-known PN sequence. Initial random seed of this PN-Sequencer should be saved for use in the extraction phase. Scrambling the logo image enhances the system security and provides a random distribution of original data. We have chosen three-level wavelet decomposition for 512x512 images. For watermark detection, we calculate the similarity between the extracted de-scrambled logo, and the original logo. A visible extracted logo, even in one of the sub-bands, can be used to prove of watermark detection. We will show in next section 4 that entropy based selection of coefficients in the embedding phase makes the proposed method a highly resistant method against attacks. As shown in Figure 1, attacks are applied to the watermarked image in several ways. The attacked image is then used in the extraction phase. Some geometrical attacks may change the size of the image. As the proposed method extraction requires input image of the same size, we resize the attacked image to the original image. In brief, we can summarize the embedding scheme in the following steps: Initialize PN-sequencer with a random value and save it. Scramble the logo image with the generated sequence in step 1, bit by bit for improving the security and robustness of algorithm. Apply DWT on the original image in three levels and in each four sub-bands. Calculate the entropy value in each sub-band to adust the chip-rate value defines the number of wavelet coefficient to be embedded the watermark and save it. Apply watermark on selected coefficients in step 4 by using the equation [1] and [2]. Apply IDWT on the wavelet coefficient to reconstruct the watermarked image. As well as, the extracting scheme can be summarized in the following steps: Initialize PN-sequencer with a random value and save it. Apply DWT on the input image in three levels. Select wavelet coefficient of embedded locations using equation [1] reversely. Use similarity equation to calculate its value in each subband of levels. 4. Performance evaluation We use a variety of still 512x512 well-known test images in the experiments including images of faces, outdoor scenes, and textured, medical, and synthetic ones with variations in illumination, resolution and size. Performance of the proposed approach is evaluated under several attacks, including JPEG compression, dither, salt & pepper, speckle and Gaussian noises, cropping, rotating, filtering, motion, disk, un-sharping, gray-scaling, resizing and rotateresizing. Figure 2, shows some of test images, which used in evaluation of MYWA method. Figure 3, shows pepper image with its watermarked positions. Figure 4 shows the watermark detection (similarity) responses in different sub-bands of different levels for the Pepper image among 00 randomly generated watermarks under JPEG attack with 30% ratio. Scrambling the AUT logo image with

the initial seed 0000001 produces the original watermark. All single distinguished peaks in the similarity responses at different levels are greater than 6. Figures 5 to 8 show the values for Barbara512 image under JPEG compression, rotate, Gaussian noise and cropping attacks with different parameters. As shown, the increase is proportional to the similarity criterion. Figure 9, shows the extracted watermark under Gaussian noise attack, which is very close to the original logo image. Figure 2. Part of MYWA test images Figure 3. Pepper watermarked & difference image We have compared our method (MYWA) with some other relative works (Cox & al., 1997, Barni & al., 1999, Zhu & al., 1999). The experimental results show the robustness of the watermark against some attacks on watermarked image illustrated in Table 2 with pepper512 image. Also, the corresponding detector response of watermarked image on our method and other relative works has been depicted. We have selected some methods, which cover both DCT and DWT decomposition in their algorithms, Barni & al., 1999 and Zhu & al., 1999 use DWT decomposition and Cox & al., 1997 use DCT characteristic. We have observed that our method is robust against all well-known attacks in the image watermarking literature, except very high compression. Figure 4. at A3, H3, V2 and D1 sub-bands 18 16 14 12 8 6 rate with peg attack 4 27 28 29 30 31 32 33 Figure 5. JPEG with {40, 15, 5, 3:1} ratio

0 90 80 70 60 50 40 30 20 rate with rotation attack 0 12 13 14 15 16 17 18 19 20 21 Figure 6. Rotate with {, 6, 2, 1} degree 0 90 80 70 60 50 40 30 20 noise ratio with Gaussian attack 0 15 20 25 30 35 Figure 7. Gaussian Noise {0.1, 0.01, 0.001, 0.0001} 90 80 70 60 50 40 30 20 noise rate with crop attack 0 8 9 11 12 13 14 Figure 8. Cropping of Barbara-512 image in sizes of {(450x500), (400x450), (350x400), (300x350)} Figure 9. Extracted logo for Gaussian noise 5. Conclusions We described a unified approach to digital watermarking of images using DWT. Adding a scrambled watermark to selected coefficients in an adaptive entropy-based way, and its robustness under several attacks, makes it a good candidate for future application. This scheme can be easily extended to color images and video image watermarking. The method shows a better performance in watermark detection than the current comparable well-known schemes. Table 2. Comparison Table Gaussian Var Correlation Detect Barni 0.01 66.98 1689 Yes psnr(org,dwt)=83.8 0.1 59.17 1308.4 Yes Zhu (T==6) 0.01 68.29 7.59 Yes psnr(org,dwt)=88.9 0.1 59.59 2. NO DCT 0.01 68.04 0.9806/1 Yes psnr(org,dwt)=83.8 0.1 59.57 0.888/1 Yes MYWA 0.01 20.11 0.9862 Yes psnr(org,dwt)=37.6 0.1 11.43 0.9066 Yes JPEG Ratio Correlation Detect Barni 15:1 75.67 41.39 Yes psnr(org,dwt)=83.8 40:1 72.01 17.12 NO Zhu (T==6) 15:1 75.55 8.29 Yes psnr(org,dwt)=88.9 40:1 71.97 3.37 NO DCT 15:1 74.85 0.9855/1 Yes psnr(org,dwt)=83.8 40:1 71.82 0.9754/1 Yes MYWA 15:1 27.91 0.9971 Yes psnr(org,dwt)=37.6 40:1 24.24 0.9928 NO Rotation Degree Correlation Detect Barni 6 59.97 0.46 NO psnr(org,dwt)=83.8 58.52-4.15 NO Zhu (T==6) 6 59.98 0.16 NO psnr(org,dwt)=88.9 58.53 0.17 NO DCT 6 60.00 0.4542/1 NO psnr(org,dwt)=83.8 58.52 0.224/1 NO MYWA 6 12.85 0.8750 Yes psnr(org,dwt)=37.6 11.40 0.8522 Yes Cropping Image Size Correlation Detect Barni 350x400 56 00 Yes psnr(org,dwt)=83.8 300x350 55.02 936.336 Yes Zhu (T==6) 350x400 56.00 0.26 NO psnr(org,dwt)=88.9 300x350 55.02 0.99 NO DCT 350x400 56.00 0.4522/1 NO psnr(org,dwt)=83.8 300x350 55.01 0.4847/1 NO MYWA 350x400 8.88 0.6626 Yes psnr(org,dwt)=37.6 300x350 7.89 0.5894 Yes References - Ambroze & al., 2001, Turbo code protection of video watermark Channel, IEEE Proc. Vis. Image Signal Processing, Vol. 148, No. 1, February 2001, 54-58. - Barni & al. A DWT-based technique for spatiofrequency masking of digital signatures, In Ping Wah Wong, editor, Proceedings of the 11 th SPIE Annual Symposium, Electronic Imaging 99, Security and Watermarking of multimedia Contents, vol. 3657, pp. 31-39, San Jose, CA, USA, Jan 1999. - Bartolini & al., 1998, Mask Building for Perceptually Hiding Frequency Embedded Watermarks, Proc. Int. Conf. on Image Processing, Oct. 1998, vol. I, pp. 450-454.

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