CHAPTER-4 WATERMARKING OF GRAY IMAGES

CHAPTER-4 WATERMARKING OF GRAY IMAGES 4.1 INTRODUCTION Like most DCT based watermarking schemes, Middle-Band Coefficient Exchange scheme has proven its robustness against those attacks, which anyhow, do not attack on the perceptual quality of image (Refer Section 2.2.2.1). For example, JPEG compression reduces the size of image considerably without having much distortion in visual quality. Therefore, most of the DCT based schemes are robust against JPEG compression attack. But in most of the research literature available, even if quality of extracted watermark logo is good enough to prove the ownership, PSNR value of extracted watermark logo is less. In this chapter, we have explained how PSNR value of extracted logo from watermarked image could be increased if watermarked image has been attacked by JPEG compression attack. Then we developed a watermarking scheme to increase the robustness against Histogram equalization attack, which attacks on perceptual quality of image. After developing the watermarking schemes which are robust against JPEG compression and histogram equalization attack, we developed a watermarking scheme which is collusion attack resistant by introducing redundancy in Middle Band Coefficient Exchange scheme. This scheme is not only collusion attack resistant but more robust against JPEG compression attack as compared to other similar state-of-the-art watermarking schemes. 61

4.2 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEMES AGAINST JPEG COMPRESSION Two, classical DCT and DWT based watermarking schemes have already been discussed in Section 2.2.2.1 and 2.2.3.1. We watermarked the images of Lena, Mandrill and Pepper, which are shown in Figure 3.11, by applying both the schemes. While watermarking the chosen images, we used a monochrome logo as a copyright data (or watermark), which is shown in Figure 3.13. Then, watermarked images, obtained by applying the above said watermarking schemes, were compressed by JPEG low compression (Quality factor, Q = 20). From the JPEG compressed images, the watermark data was recovered. As it has already been mentioned that DCT and DWT based watermarking schemes are robust against JPEG attack, we found that extracted watermark logo is quite detectible to prove the ownership as shown in Figure 4.1. (a) (b) Figure 4.1 (a): Extracted watermark logos from test images of Lena, Mandrill and Pepper by applying DCT based scheme (b): Extracted watermark logos from test images of Lena, Mandrill and Pepper by applying DWT based schemes Though, the extracted watermark logos are quite detectible, we can see the presence of noise in extracted watermark logos and therefore the PSNR values of extracted watermark logos are less. Therefore, there is a possibility to further improve the quality of the extracted watermark logos with an increased PSNR value of extracted watermark logos. 62

To achieve this, we propose to change the image data or image pixel values such that it has less impact of JPEG compression attack after getting watermarked without loosing the perceptual quality to a great extent. We thought to change or modify the image such that the affect after the attack on the watermarked image could be minimized. We tried to accomplish this by creating the same effect in an image, before watermarking it, which this image shall have, after it has been attacked. More precisely, if we know that our watermarked image may have to suffer JPEG compression attack, whatever changes will be made by JPEG attack in the watermark image, we tried to incorporate those changes in the pixel values in advance so that changes caused by JPEG compression attack may be minimized. This led to the preprocessing of the images, i.e., doing some modifications in the image which are equivalent to the attack before we start watermarking on it, either by using DCT or DWT based watermarking schemes. To implement the idea, we decided to analyze the JPEG compression attack on an image which has been watermarked by DCT and DWT based watermarking schemes. We proposed three transformation steps before the watermarking of an image, which are as follows: 1) Take the gray level image which has to be watermarked; 2) Compress it using JPEG scheme; and 3) Convert back the compressed image to gray level image to get the Transformed Image. We applied the above 3 transformation steps on our chosen test images. First, we generated 3 transformed images of Lena s test image by keeping the JPEG quality factor Q = 20, Q = 40 and Q = 60. Then, in the same way, we generated 3 transformed images of remaining 2 test images of Mandrill and Pepper also. Then, we watermarked transformed images as well as original images, using both schemes stated above. So, total 12 images were watermarked separately by DCT as well as DWT based watermarking schemes. For each of the 3 test images, 4 copies of it were watermarked where 1 copy was the original image and other 3 copies were the transformed 63

images, generated by our proposed preprocessing steps. All watermarked images were then compressed using JPEG low compression (Q = 20). After retrieving the watermark logos, it was found that the quality of extracted watermark logos from transformed images was better than the quality of extracted watermark logos from original images. Table 4.1 summarizes the PSNR values (in decibel) of extracted watermark logos. It may be observed that for the test image Lena, PSNR values of extracted logos were better from all 3 transformed images as compared to PSNR value of extracted logo from original image for both DCT as well as DWT based watermarking schemes. But for the test images Mandrill and Pepper, only 1 transformed image generated by keeping Q = 40, gave the batter PSNR value of extracted logo as compared the PSNR value of extracted logo from their original image for both DCT as well as DWT based watermarking schemes. Thus, we conclude that the preprocessing for a certain Q enhances the quality of extracted logos to some extent and, therefore, to increase the robustness of watermarking schemes against some well known attacks, we must analyze the attack s characteristics and its impact on the image and then adjust or preprocess the image in such a manner that the impact of the attack could be minimized. 4.3 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEME AGAINST HISTOGRAM EQUALIZATION ATTACK In the previous section, we had discussed about the preprocessing of an image to improve the robustness of DCT and DWT based watermarking schemes against JPEG compression. We know that transformed domain based watermarking schemes like DCT and DWT based schemes which were under our consideration in previous section, are robust against the attacks which do not change the perceptual quality of an image like JPEG compression attack. We have seen that by our proposed preprocessing, a watermarked image became more resistant to JPEG compression attack. We decided to 64

see the effectiveness of our proposed idea of preprocessing against those attacks which alter the image perceptually. So, we focused on the histogram equalization attack. If we equalize the histogram of an image, it is affected badly. We would now check whether our proposed idea of preprocessing works in the case of histogram equalization? Table 4.1: PSNR (in decibel) of extracted watermark logo from JPEG compressed (Q = 20) watermarked image Results given by watermarking of original image Results given by watermarking of transformed image. Image Scheme Used PSNR of Extracted Logo if Original Image is not Transformed PSNR of Extracted Logo if Original Image is Transformed using Q = 20 PSNR of Extracted Logo if Original Image is Transformed at Q = 40 PSNR of Extracted Logo if Original Image is Transformed at Q = 60 Lena DCT 31.5694 31.5712 31.5712 31.5742 DWT 30.906 30.924 30.918 30.924 Mandrill DCT 31.6266 31.6254 31.6284 31.6214 DWT 30.888 30.87 30.894 30.888 Pepper DCT 31.5892 31.5878 31.5906 31.5876 DWT 30.598 30.58 30.604 30.598 We watermarked the images of Lena, Pepper, Mandrill and Barbara, which are shown in Figure 3.11, by applying both DCT and DWT based watermarking schemes. While watermarking the chosen images, we used a monochrome logo as a copyright data (or watermark) which is shown in Figure 3.13. Then, for all watermarked images obtained by applying the above said watermarking schemes, we equalized their histogram and then recovered the watermark data from the histogram equalized images. We found that extracted watermark logos were quite detectible to prove the ownership, as shown in Figure 4.2, but all were very noisy. We now preprocess the image through the following proposed 3 transformation steps before watermarking the images: 65

1) Take the gray level image to be watermarked; 2) Adjust the image such that its histogram is equalized to get the Transformed Image ; and 3) Apply watermarking DCT and DWT based schemes to the image obtained in step 2. We applied the above 3 transformation steps on our chosen test images. We generated transformed images of Lena, Pepper, Mandrill and Barbara test image. For each of the 4 test images, 2 copies of it (1 copy of the original image and other copy of the transformed images generated by our proposed preprocessing steps) were watermarked by DCT and DWT based watermarking schemes. The histograms of all watermarked images were then equalized. After retrieving the watermark logos, it was found that the quality of extracted watermark logos from transformed images was better than the quality of extracted watermark logos from original images. (a) (b) Figure 4.2 (a): Extracted watermark logos from test images of Lena, Mandrill, Pepper and Barbara by applying DCT based scheme (b): Extracted watermark logos from test images of Lena, Mandrill, Pepper and Barbara by applying DWT based schemes Table 4.2 summarizes the PSNR values (in decibel) of extracted watermark logos. It may be observed that, the watermark logos, extracted from watermarked transformed 66

images have PSNR values slightly better then the PSNR values of extracted logos retrieved by watermarked original image. Even if, PSNR values were increased slightly, considerable improvement in perceptual quality was observed. Figure 4.3 shows the extracted logos from histogram equalized attacked watermarked images. Logos at left sides are recovered form attacked watermarked original image, whereas logos at right side in the figure are recovered logos form watermarked transformed images. We can easily find that quality of extracted watermark logos from transformed images is better for all the 4 test images. Table 4.2: PSNR of extracted log from attacked test images PSNR (DB) Watermarking PSNR of Extracted PSNR of Extracted Logos scheme used Logos from Original from Transformed Image Images Image DCT 26.43 26.446 Lena DWT 25.79 25.81 DCT 26.378 26.412 Pepper DWT 25.245 25.251 DCT 26.454 26.498 Mandrill DWT 25.887 25.912 DCT 26.122 26.156 Barbara DWT 25.567 25.58 67

Figure 4.3: Extracted logos from original image (left) and transformed image (right) of Lena, Mandrill, Pepper and Barbara s (Top to Bottom) histogram equalized images (By applying DCT based scheme) Therefore, we conclude that preprocessing the images to minimize the impact of histogram equalization attack, made the test images more robust against said attack if DCT and DWT watermarking schemes were used. This favors our statement made in the previous section that we must analyze the attack s characteristics and its impact on the image and then adjust or preprocess the image in such a manner that the impact of the attack could be minimized. 4.4 DEVISING A COLLUSION ATTACK RESISTANT WATERMARKING SCHEME FOR IMAGES USING DCT After developing a technique to make DCT and DWT based watermarking schemes (discussed in Section 2.2.2.1 and 2.2.3.1) more robust against JPEG compression and histogram equalization attacks, we considered a malicious attack, the collusion attack which was discussed in Section 2.4.1. Seeing the financial implications of this attack, we propose a new term or benchmark for watermarking schemes, the ICAR i.e. 68

Inherently Collusion Attack Resistant. We recommend that any watermarking algorithm, by definition, must be collusion attack resistant in nature. A watermarking scheme must be first ICAR and then it should focus on other common image manipulations and malicious attacks. Henceforth, all watermarking schemes that we are present are ICAR in nature. The classical Middile Band Coefficient Exchange (MBCE) scheme, A DCT based scheme discussed in Section 2.2.2.1, is known to be robust against common image manipulations and JPEG compression attack. But this scheme, however, cannot sustain collusion attack. If, any attacker takes more than one copy of a watermarked image, then by analyzing the patterns of block DCT coefficients, attacker can easily predict the watermark location and watermark data. Our aim is to develop an ICAR watermarking scheme which can sustain other common image manipulations and known attacks also over the existing MBCE scheme which is not capable of sustaining collusion attack. For developing the new ICAR scheme, the following 2 issues were kept in mind: 1) If only one pair is used to hide the watermark data, it might happen that by an attack or by any image manipulation, values of this pair are modified. So, instead to exchanging only one pair of coefficients from FM region, we should exchange more than one pair of the coefficient i.e. introduce some redundancy; and 2) To achieve ICAR nature in watermarking scheme, we must ensure that every copy of watermarked image has a different pattern of hiding watermark data so that attacker can not conclude the location and content of watermark data even after analyzing many copies of watermarked image. Issue no.1 is resolved as follow: There are 22 coefficients in the FM region in an 8 x 8 DCT block. Out of these 22 coefficients, we can form 17 pairs having nearly the same values in their corresponding 69

JPEG quantization table. Therefore, to introduce redundancy in MBCE scheme, we had a choice to exchange the n pairs where the value of n ranges from 1 to 11(as there are total 22 coefficients). We can not disturb or modify all 22 coefficients as it will affect the image perceptibility. We conducted some experiment on this issue and found that if we modify the values of 8 coefficients (i.e. 4 pairs are exchanged), no much degradation in the image perceptibility is recorded. Accordingly, we decided to set the value of n equal to 4. Issue no. 2 is simply resolved by choosing the combination of 4 pairs randomly in each watermarked image. MBCE scheme exchanges 1 pair of coefficient from FM region to hide 1 or 0. For example, if coefficients at (3,2) and (2,3) are decided to hide the watermark data, this scheme sets DCT (3,2) > DCT (2,3) to interpret 1 and set DCT (3,2) < DCT (2,3) to interpret 0 by exchanging the coefficient values. While decoding the watermark data, MBCE scheme takes 8 x 8 DCT of watermarked image and by looking the relative strength of the coefficients at these locations, it decodes the 1 or 0 to reconstruct the watermark data. The proposed ICAR scheme exchanges 4 pairs which indicates that either 0 or 1 is hidden in the block. One such combination of 4 pairs may be taken as: {(5,1) and (4,2), (6,3) and (5,4), (5,2) and (4,3), (3,2) and (2,3)}. A scheme is robust if it is able to recover watermark data even if most of the middle band conefficients are attacked. To achieve this, we need to develop some dependencies on low frequecny coefficients also. In Figure 2.3 and Figure 2.4, values present at location (0,1) and (1,0) in 8x8 block DCT are low frequency coefficients of an image and attacker can not change the values at these locations because it will affect the image badly. 70

Figure 4.4: Swapping of 4 pairs to hide 0 or 1 in conjunction with low frequency values We developed a scheme of exchanging 4 middle-band coefficient pairs in strong correlation with low frequency coefficients such that even if attacker successfully attacks on 3 pairs, only 1 pair of coefficient will decode the watermark data correctly. Swapping criteria of the proposed scheme is illustrated in Figure 4.4. More details of encoding and decoding process are given in Section 4.4.2 and 4.4.3. The proposed watermarking scheme can be defined as a 7-tuple (X, W, P, K, G, E, D), where 1) X denotes the set of instances X i, of a particular gray level image, (If N copies of an image are to be watermarked, then 0 i N); 2) W denotes the monochrome watermark logo; 3) P denotes the set of policies P i, 0 i N; 4) K denotes the watermark strength parameter; 5) G denotes the policy generator algorithm G: X i P i, where each X i will have a unique P i, i.e. a different policy to hide the watermark data; 6) E denotes the watermark embedding algorithm, E: X i x W x P i X i ; 71

7) D denotes the watermark detection algorithm, D: X i x P i W, where W represents extracted watermark. Out of these 7 tuples, last 3 tuples are algorithms as discussed below: 4.4.1 G, THE POLICY GENERATOR ALGORITHM To watermark each copy X i of an image X differently, we need a different watermarking policy. Here Policy means that for every copy of the image, there will be unique combination of 4 pairs of middle band coefficients. To generate a policy, we simply take 8 x 8 DCT of the input image X i and randomly select 4 pairs out of 17 pairs of middle band region. So, number of policies that can be generated are 17 C 4 = 2380 which means that 2380 copies of a single image can be watermarked such that no two watermarked images have same policy. This step ensures that attacker can not conclude the location of watermark data by colluding many watermarked copies of an image. This also depicts that our proposed scheme is an ICAR scheme. 4.4.2 E, THE WATERMARK EMBEDDING ALGORITHM In this algorithm, each 8 x 8 DCT block of an image is used to hide a single bit of watermark logo. This algorithm is given as below: 1. Repeat steps 2 to 13 for i = 1..n; // where n is the number of copies of a single image to be watermarked // 2. INPUT (X i ); 3. Take 8 x 8 block DCT of X i ; 4. INPUT (W); 5. Convert W into a string S = (S j S j = {0, 1}, for j = 1..length of the watermark); 6. Let L = STRING_LENGTH (S); // where L is the length of watermark data. If L=1000, then first 1000 DCT block of Xi are used // 72

7. P i = CALL (G); // Each Pi shall be stored in an author s database for the detection purpose in future. Let the Pi, for chosen Xi, be {(5,2) and (4,3), (6,3) and (5,4), (5,1) and (4,2), (3,2) and (2,3)} // 8. Repeat steps 9 to12 for r = 1..L; 9. Read S r ; 10. If S r = 0 If (DCT (0, 1) > DCT (1, 0)) Swap the DCT coefficients from chosen Pi such that coefficients at (5,2), (6,3), (5,1) and (3,2) become larger than (4,3), (5,4), (4,2) and (2,3) respectively; If (DCT (0, 1) <= DCT (1, 0)) Swap the DCT coefficients from chosen Pi such that coefficients at (5,2), (6,3), (5,1) and (3,2) become smaller than (4,3), (5,4), (4,2) and (2,3) respectively; Else If S r =1 If (DCT (0, 1) <= DCT (1, 0)) Swap the DCT coefficients from chosen Pi such that coefficients at (5,2), (6,3), (5,1) and (3,2) become larger than (4,3), (5,4),(4,2) and (2,3) respectively; If (DCT (0, 1) > DCT (1, 0)) Swap the DCT coefficients from chosen Pi such that coefficients at (5,2), (6,3), (5,1) and (3,2) become smaller than (4,3), (5,4), (4,2) and (2,3) respectively; End; 11. For all swapped coefficients pairs repeat the step 12; 12. If (DCT (u 1, v 1 ) DCT (u 2, v 2 ) > K) If (DCT (u 1, v 1 ) > DCT (u 2, v 2 )) DCT (u 1, v 1 ) = DCT (u 1, v 1 ) + K/2; 73

Else End; DCT (u 2, v 2 ) = DCT (u 2, v 2 ) - K/2; DCT (u 1, v 1 ) = DCT (u 1, v 1 ) - K/2; DCT (u 2, v 2 ) = DCT (u 2, v 2 ) + K/2; // Like Classical MBCE scheme (Section 2.2.2.1), robustness of the watermark can be improved by using a watermark strength constant K such that for all 4 chosen pairs, DCT (u 1, v 1 ) DCT (u 2, v 2 ) > K. If coefficients do not meet these criteria, they should be modified by using some random noise to satisfy the relation. Increasing K thus reduces the chance of detection errors at the expense of additional image degradation. This ensures that larger coefficients remains larger even after image manipulations because coefficients relative values will decide the decoding of the watermark data // 13. Take IDCT to reconstruct X i ; 14. End. 4.4.3 D, THE WATERMARK DETECTION ALGORITHM We decode 1 and 0 based on the swapping criteria shown in Figure 4.4. The detection algorithm steps are as follows: 1. INPUT (X i ); // Xi is the attacked copy of a watermarked image // 2. Take 8 x 8 block DCT of X i ; 3. For each P i in author s database, repeat the steps 4; // If initially 10 copies were watermarked, then out of 10 policies, for 1 policy, watermark will be recovered correctly. To explain further steps, we are assuming that now algorithm is in a loop where Pi is {(5,2) and (4,3), (6,3) and (5,4), (5,1) and (4,2), (3,2) and (2,3)}, which was used to watermark this particular Xi // 4. Repeat the steps 5 for j = 1.L; 74

// L is the length of watermark data. A single bit will be recovered form one 8x8 DCT block // 5. Take j th DCT block to form j th bit of watermark as follows: If (DCT (1, 2) > DCT (2, 1)) If (DCT (5, 2) > DCT (4, 3)) T1 = 1; else T1 = 0; If (DCT (5, 1) > DCT (4, 2)) T2 = 1; else T2 = 0; If (DCT (6, 3) > DCT (5, 4)) T3 = 1; else T3 = 0; If (DCT (3, 2) > DCT (2, 3)) T4 = 1; else T4 = 0; If (T1 + T2 + T3 + T4 > 1) Decode 0 ; Else decode 1 ; Else If (DCT (1, 2) <= DCT (2, 1)) If (DCT (5, 2) > DCT (4, 3)) P1 = 1; else P1 = 0; If (DCT (5, 1) > DCT (4, 2)) P2 =1; else P2 = 0; If (DCT (6, 3) > DCT (5, 4)) P3 = 1; else P3 = 0; If (DCT (3, 2) > DCT (2, 3)) P4 = 1; else P4 = 0; If (P1 + P2 + P3 + P4 > 1) Decode 1 ; Else decode 0 ; End; 6. Store W, the recovered watermark; 7. End. 75

Even if three pairs are attacked to confuse the decoder, only one pair in conjunction with the relationship between DCT (1, 2) and DCT (2, 1), enables us the detection of 1 or 0. That is why the line (T1 + T2 + T3 + T4 > 1) is written. If there is no change in watermarked image, all values will remain unaffected and we can set the condition (T1 + T2 + T3 + T4 > 3). 4.4.4 PERFORMANCE OF THE PROPOSED SCHEME To incorporate the ICAR nature, we have introduced redundancy and randomness in classical MBCE scheme. Because of this attacker has no mechanism to conduct pattern analysis to find out the location of the watermark data. Therefore we can say that the proposed scheme s design ensures that pattern analysis by colluding many watermarked copies is not possible and thus the scheme is ICAR. Now, in order to check that injecting the ICAR nature in the scheme did not degrade the performance against common image manipulations and known attacks, we tested our scheme on 3 well known test images of Lena, Mandrill and Pepper of size 512 x 512 and 256 colors in Windows BMP format as shown in Figure 3.11. We generated the watermarked copies at various watermark strength constant K. Values of K were chosen from 10 to 50, and then for all watermarked copies, watermark logos were recovered. Obviously, for higher values of K, the quality of extracted watermark logos were fine but the quality of watermarked image itself, was affected much. On the other hand, for the lower values of K, the watermarked image generated were of finer quality but the quality of extracted watermark logos from such images was poor. This is an obvious Imperceptibility versus Robustness trade-off. It was observed that, the value K = 20 was the best value under the circumstances. For this value of K, the recovery was good without losing much image quality. So, further tests were conducted by using K = 20. 4.4.4.1 PERFORMANCE AGAINST JPEG COMPRESSION: All watermarked test images were compressed using JPEG scheme at various JPEG quality factors. Even with quality factor, Q = 20 (9.1 % of original size, JPEG Low compression), extracted logos were quite detectible. Table 4.3 summarizes the PSNR values of extracted watermark 76

logos from JPEG compressed watermarked images. Figure 4.5 shows the extracted watermark logos from JPEG compressed watermarked test images. It may be observed from both the specified table and the figure that our proposed scheme is capable of sustaining JPEG compression attack and even at Q = 20, the recovery of the watermark logo is quite efficient. 4.4.4.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS: All watermarked test images were then tested against Horizontal flip, Scaling, Brightness / Contrast (both - 20 to + 20) adjustment and Noising. Our scheme sustained all above image manipulations. Figure 4.6 shows the extracted watermark logos recovered by the test image of Lena, which had undergone all the above stated attacks. Same results were found for other 2 test images also. Table 4.3: PSNR of extracted watermarks after JPEG compression PSNR (DB) Quality Lena Watermarked Mandrill Pepper factor Image Watermarked Image Watermarked Image 80 23.724 23.736 23.73 60 23.715 23.7315 23.724 40 23.6955 23.7285 23.706 30 23.697 23.724 23.7075 20 23.6775 23.7195 23.6925 Figure 4.5: Extracted watermark logos after JPEG compression at Q = 20 from watermarked Lena, Mandrill and Pepper images 4.4.4.3 COMPARATIVE STUDY WITH OTHER MECHANISMS: We compared the performance of the proposed scheme for the JPEG compression with other similar 77

state-of-the-art methodologies which are well known for their robustness against JPEG compressions. Schemes chosen were as follows: Scheme-A: Correlation based Schemes with 1 PN sequence (Section 2.1.3.1) Scheme-B: Correlation based Schemes with 2 PN sequence (Section 2.1.3.2) Scheme-C: DCT Domain based Scheme (Section 2.2.2.1) Scheme-D: DWT Based Scheme. (Section 2.2.3.1) Watermarked images, obtained by proposed scheme as well as by other four schemes (Scheme-A to Scheme-D) were then compressed at various JPEG quality factors. We named our proposed scheme as Scheme-E. As all the above said watermarking schemes were robust against the JPEG compression attack, we evaluated them at different scale. All schemes were evaluated for how rapidly the scheme would start losing its robustness as the JPEG quality factors goes down. It was observed that up to Q = 40, performance of all watermarking schemes were approximately equal but for lower values of JPEG quality factor (Q < 40), our scheme showed more resistant as compared to scheme-a and scheme-b. The percentage decrease in quality of extracted watermark with respect to JPEG quality factors were compared as shown in Figure 4.7. It may be observed that performance of proposed scheme is better then Scheme A and Scheme B for low JPEG compression. Proposed scheme loses its performance as compared to DCT and DWT based schemes because we are increasing robustness against collusion attack (by making it ICAR) at the expanse of quality (by introducing redundancy). Figure 4.6: Extracted watermark logos from Lena s image after Horizontal flipped, scaled, brightness /contrast adjusted and Noising (Left to Right, Top to bottom) 78

120 100 80 60 40 20 Schema-A Schema-B Schema-C Schema-D Schema-E 0 Q80 Q60 Q40 Q30 Q20 Figure 4.7: Percentage decrease in quality of extracted watermark with respect to JPEG quality factor So, even after introducing redundancy in classical MBCS scheme to fight against collusion attack, quality of recovered watermark does not decrease very much as compared to Scheme-C and Scheme-D and better than Scheme-A and Scheme-B. We, therefore, conclude that our proposed ICAR watermarking scheme is quite robust against JPEG compression and common image manipulations for watermarking of gray BMP images. 4.5 CONCLUSION To summarize this chapter, we can say that if DCT and DWT based watermarking schemes discussed in Section 2.2.2.1 and 2.2.3.1 are to be used for the watermarking of a gray BMP image, then the image becomes more resistant to JPEG compression attack if we transform the original image to JPEG image at certain JPEG quality factor and then convert it back to gray level image. Similarly, if we preprocess the image in such a way that its histogram is equalized, then also an image become more resistant to histogram equalization attack for the same watermarking schemes. So, a modification in the image such that the affect after the attack on the watermarked image could be minimized, increases the robustness of schemes for DCT and DWT based watermarking schemes. Then, we developed a DCT based ICAR watermarking scheme which was very robust against JPEG compression attack and other common image manipulations. 79