2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Fragile Watermarking-based Authentication Scheme for Demosaicked Images
Chia-Chen Lin*
Xiao-Long Liu
Dept. of Computer Science and Information Providence University Taichung, Taiwan E-mail:
[email protected]
Dept. of Computer Science National Chiao Tung University Hsinchu, Taiwan E-mail:
[email protected]
Cheng-Han Lin
Shyan-Ming Yuan
Dept. of Computer Science and Information Management, Providence University Taichung, Taiwan E-mail: s1012565@ pu.edu.tw
Dept. of Computer Science National Chiao Tung University Hsinchu, Taiwan E-mail:
[email protected]
watermarking techniques. In particular, over the last 10 years, several image authentication schemes [6, 7, 8] have been developed for the purpose of locating the tampered areas. One representative fragile watermarking scheme is least significant bit (LSB) substitution [8], where authentication bit is embedded in the LSB of each pixel. However, the localization capability is barely satisfactory, and not every modified pixel is guaranteed to be correctly detected.
Abstract—This paper presents a reversible fragile watermarking scheme for the authentication of demosaicked images. Each authentication code is embedded into the two rebuilt components of each color pixel by using a designed authentication table (ACT). The utilization of ACT secures the watermarking procedure and the watermarked image can be recovered back to the original demosaicked image by the proposed scheme. The proposed scheme is compared with some existing image authentication schemes, and the experimental results demonstrate that our scheme can preserve better image quality and provides superior tamper detection performance.
Nowadays, digital still cameras are wildly used to capture digital images in the real world. In general, a digital camera needs three sensors with three color filters to capture the R, B and G component of each color pixel, respectively. However, the hardware cost of having three sensors in a digital camera is quite expensive. Therefore, a single sensor with a grid of different color sensors, called a color filter array (CFA) [9], is used to reduce hardware costs. The commonly used pattern for a CFA is the Bayer pattern, as shown in Fig. 1, where only one component, i.e., red, blue or green, of each color pixel is significantly sampled by the corresponding grid. Inevitably, the use of CFA results in missing the other two components in each color pixel. To reconstruct the missing color components in each color pixel, an interpolation process called image demosaicking or CFA interpolation [10] is performed on the CFA sampled data. Therefore, various image demosaicking schemes [11, 12] result in varying reconstruction performance, and image demosaicking is still an ongoing technique under development. The classical image demosaicking schemes include: pixel copy, bilinear interpolation and cubic spline interpolation.
Keywords- fragile watermarking; reversible authentication table (ACT); tamper detection; image demosaicking
I.
INTRODUCTION
With the tremendous growth in networks, digital image transmission on the Internet has also become ubiquitous. Although this is convenient for human social interaction, almost any person can illegitimately intercept an image and tamper with it as the image is transmitted over the Internet. Since the integrity of a transmitted image is easily breached, research regarding image integrity protection has become increasingly urgent. The concept of image authentication [1] seeks to protect the integrity of digital images. Fragile watermarking [2, 3] is one of several techniques for image authentication. In this type of watermarking, a fragile watermark is embedded into the cover image. It is designed so that the embedded watermark is readily damaged once the cover image is attacked by any kind of manipulation. Therefore, if an image is suspected of being manipulated, the tampered areas of that suspicious image can be easily detected by extracting the embedded watermark. Previous watermarking related literature [4, 5] in the last century mainly focused on detecting whether or not an image had been tampered. However, researchers did not clearly identify where an image may have been modified. In contemporary times, having a tamper locating capability is a major concern that is being addressed in fragile 978-1-5090-0188-0/15 $31.00 © 2015 IEEE 978-0-7695-5668-0/15 DOI 10.1109/IIH-MSP.2015.98
Figure 1. Bayer pattern of 4×4 pixels.
In order to protect the integrity of demosaicked images, a simple fragile watermarking based image authentication 97
scheme for image demosaicking was proposed by Hu et al. [13]. In their scheme, a fragile watermark was embedded to each color component, i.e., R, G and B, of the demosaicked pixels. Although good tamper detection accuracy can be achieved by using this scheme, the visual quality of the watermarked image is unsatisfactory. The distortion also enlarges when increasing the length of the fragile watermark. Furthermore, the watermarked image cannot be recovered back to the original demosaicked image if the image is authenticated as un-tampered.
The other two components are not from the sample, but are rebuilt by the image demosaicking process, we denote them as r1 and r2. Authentication code ac is embedded into the rebuilt components r1 and r2 by using the designed authentication table ACT.
This paper proposes a new efficient fragile watermarking based authentication scheme for demosaicked images that provides reversibility of a demosaicked image, while improving tamper detection accuracy and the visual qualities of the embedded image. In the proposed scheme, each authentication code is embedded into the two rebuilt components of each color pixel by using a designed authentication table. If the watermarked image is tampered, the tampered areas of the image will be accurately detected, and if otherwise, the watermarked image can be recovered back to the original CFA form if necessary. The proposed scheme is compared with the LSB substitution scheme and Hu et al.’s scheme, and the experimental results demonstrate that our method preserves the highest image quality while maintaining outstanding tamper detection accuracy.
Figure 2. Example of a 3×3 authentication table.
To embed each authentication code ac into r1 and r2 of each color pixel P, the authentication value av of this pixel is first computed by Eq. (1): = [ MOD 3][ MOD 3].
(1)
where [r1 MOD 3] and [r2 MOD 3] represent the row and column in authentication table ACT, respectively. If the computed av equals ac, no change is made on this pixel. Otherwise, we need to adjust the rebuilt component values r1 and r2 so that the value of [ MOD 3][
MOD 3] is equal to authentication code ac. The adjustment of (r1, r2) into ( , ), should meet the following conditions:
In Section 2, we provide a detailed exposition of the proposed scheme. Experimental results and discussions are presented in Section 3. Finally, a conclusion is given in Section 4.
x x x
II. THE PROPOSED SCHEME This section presents a detailed exposition of the proposed fragile watermarking scheme, which consists of watermark embedding procedure and tamper detection procedure. After obtaining a CFA sampled image captured by Bayer pattern, we can select any one image demosaicking scheme as mentioned in Section 1 to reconstruct the demosaicked RGB color image from the CFA sampled data. Then the watermark embedding procedure is applied on the demosaicked image. If the watermarked image needs authentication, the tamper detection procedure can be applied.
the value of and is between 0 and 255, [ MOD 3][ MOD 3] = ac, The modification from (r1, r2) to ( , ) is minimized, i.e. the Euclidean distance between (r1, r2) and ( , ) are minimum.
An example of the watermark embedding procedure is described by the following. Assume the authentication code ac to be embedded is 0 and the authentication table ACT is shown in Fig. 2. Given the color pixel P= (67, 70, 66), where 67 is the sampled component s by Bayer pattern, 70 and 66 are the rebuilt components r1 and r2, respectively. The authentication value av of P is ACT[70 mod 3][66 mod 3]=ACT[1][0]=1, which is not equal to ac with value 0. Therefore, (70, 66) is adjusted into (69, 66) to meet the conditions mentioned above. As a result, P= (67, 70, 66) is modified into = (67, 69, 66).
A. Watermark embedding procedure A fragile watermark with a total number of H×W authentication codes was first generated for the original H×W sized demosaicked image. The authentication codes are random values from 0 to 2, which are generated by a pseudo random number generator (PRNG). Each authentication code ac is embedded into each color pixel in the demosaicked image. To embed the watermark, a 3×3 authentication table (ACT) with values 0~2 was also predesigned. An example is shown in Fig. 2. Assume P denotes the original color pixel with RGB components in the demosaicked image, where only one component of each P is sampled by CFA, we denote it as s.
Utilization of an authentication table (ACT) secures the proposed scheme and makes it more difficult to crack the embedded watermark. Furthermore, the proposed scheme is recoverable. Since the original sampled component in each pixel is un-modified during watermark embedding, the original CFA sample data can be completely extracted from a watermarked image. Therefore, the original demosaicked image can be recovered back from the extracted CFA sample data with the same image demosaicking scheme as previously used.
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B. Tamper detection procedure The goal of the tamper detection procedure is to detect whether or not the given color image was modified, and to locate the tampered area if the given image was detected as having been modified. In detection for a H×W sized image, the original fragile watermark with a total number of H×W authentication codes ac is first generated by PRNG, which is the same as in the watermark embedding procedure. Then the extracted watermark with a sequence of H×W authentication values av is generated from the given image, where the extracted authentication value av of each pixel P= (s, r1, r2) is also computed by Eq. (1).
Figure 4. Original test images
Peak signal-to-noise ratio (PSNR) was used to estimate the visual quality of the reconstructed images, which is defined as Eq. (2), where the mean squared error (MSE) is as shown in Eq. (3):
When the original watermark and extracted watermark are available, we can determine whether or not each color pixel P has been tampered by the corresponding ac and av. If the authentication code ac is equal to extracted authentication value av, P is regarded as a clear pixel and it is marked as white. Otherwise, P is regarded as a modified pixel and marked as black.
PSNR = 10 × log
MSE =
(255) dB, MSE
(2)
1
(c(i, j) − c (i, j)) . (H × W)
(3)
As an exception, when the color pixel is modified, it may happen that the extracted authentication value av is the same as authentication code ac. Therefore, the detection procedure may miss these modified pixels. To improve the detection accuracy, an iterative tamper refinement process that was previously presented in Hu et al. [13] was employed. For each white pixel P, a 3×3 mask of it is checked. Four test cases as shown in Fig. 3 that are sequentially checked to determine whether or not the white pixel P will be changed to a black. If one of the four cases is met, white pixel P is changed to black. The tamper refinement process is iterated until there are no white pixels can be changed.
where c(i, j) and c (i, j) indicate the corresponding component values, i.e. R, G or B, in position (i, j) of the original image, and the reconstructed image, respectively. H and W represent the height and width of the image. The PSNR value of a color image is the average of the PSNR values of R, G and B components. A larger PSNR value indicates a lower difference between the original image and the reconstructed image. Table 1 presents the results for PSNR values of the demosaicked images reconstructed by bilinear interpolation and the watermarked images of the proposed scheme. The average visual qualities of the demosaicked images and the watermarked image were 29.86 dB and 29.83 dB, respectively. The image loss from the demosaicked images to the watermarked image were less than 0.1 dB, which indicates that the watermark embedding procedure of the proposed scheme incurs only a very tiny distortion to the demosaicked images.
Figure 3. Four cases for tamper refinement process TABLE I.
III.
EXPERIMENTAL RESULTS AND DISCUSSIONS
This section first presents some performance results of the proposed fragile watermarking scheme. Subsequently, performance comparisons with LSB substitution and Hu et al.’s scheme [13] are given. All experiments are performed with eight commonly used color images of the same size 512×512, as shown in Fig. 4. During the experiments, the CFA sampled images were first captured by applying a Bayer pattern to the original color images. Then a bilinear interpolation technique was selected to reconstruct the demosaicked image. Finally, the proposed scheme was applied on the demosaicked image. The experimental simulation environment was comprised of an Intel Core i7 CPU, Quad system with 8-GB RAM, Windows 7 operating system, and a JAVA programming environment.
PSNR VALUES OF BILINEAR INTERPOLATION AND PROPOSED SCHEME
Images
Airplane Baboon House Lena Peppers Sailboat Splash Tiffany Average
Bilinear Interpolation 31.30 23.61 29.17 31.73 29.94 28.27 35.08 29.80 29.86
Proposed Scheme 31.26 23.61 29.14 31.69 29.92 28.25 35.00 29.77 29.83
To show the tamper detection performance of the proposed scheme, an extra object was added on each watermarked image to test detection. Fig. 5 illustrates an example of the tamper detection results of the proposed scheme: Fig. 5. (A) is the tampered Lena image where a
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TABLE III. COMPARISON RESULTS OF DIFFERENT SCHEMES
flower was added, Fig. 5. (B) is the rough detection result and Fig. 5. (C) is the final detection result, which is improved by a tamper refinement process. A black point represents a modified pixel and a white point represents a clear pixel. It is obvious that tamper detection accuracy is significantly improved by utilizing the tamper refinement process.
Items PSNR PTD PFD
IV.
Figure 5. Example of tamper detection result
The averaging tamper detection results of the eight test images by the proposed scheme are listed in Table 2, where NMP represents the total number of modified pixels in the tampered image, NDP is the number of detected pixels, NTDP is the number of true detected pixels, NFDP is the number of false detected pixels, PTD is the percent of true detection and PFD is the percent of false detection. The percent of true detection PTD by rough detection is nearly 67% because the extracted authentication value av of a modified pixel may be the same as authentication code ac. The probability is 1/3 according to the designed authentication table. Therefore, 33% of the modified pixels may miss by the rough detection. The result of PTD can be improved to 100% by the tamper refinement process in final detection procedure. However, the tamper refinement process also incurs false detection, where several clear pixels are detected as modified pixels. The percent of false detection PTD incurs by tamper refinement process is 1.21% in the proposed scheme.
NMP NDP NTDP NFDP PTD PFD
Rough Detection Results 37523 25112 25112 0 66.93% 0%
Hu et al.’s 29.83 100% 5.84%
Proposed 29.83 100% 1.21%
CONCLUSIONS
This paper proposed a reversible fragile watermarking scheme for the purpose of tamper detection in demosaicked images. The experimental results shows that our scheme can preserve outstanding image quality after watermarking, with 100% tamper detection accuracy by utilizing a tamper refinement process. The proposed scheme was compared with LSB substitution and Hu et al.’s scheme [13], and the comparison results demonstrate that our scheme is superior in preserving optimal image quality and provides superior tamper detection performance. REFERENCES [1] S. Walton, "Image Authentication for a Slippery New Age," Dr Dobb's Journal-Software Tools for the Professional Programmer, vol. 20, no. 4, pp. 18-27, 1995. [2] P. W. Wong, N. Memon, "Secret and public key image watermarking schemes for image authentication and ownership verification," IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1593-1601., 2001. [3] A. Haouzia, R. Noumeir, "Methods for image authentication: a survey," Multimedia Tools and Applications, vol. 39, no. 1, pp. 1-46, 2008. [4] M. Yeung, F. Mintzer, "An invisible watermarking technique for image verification," in Proceedings of the International Conference on Image Processing, 1997. [5] J. Fridrich, M. Goljan, A. C. Baldoza, "New fragile authentication/watermark for images," in Proceedings of the International Conference on Image Processing, 2000. [6] C. C. Chang, Y. P. Hsieh, C. H. Lin, "Sharing secrets in stego images with authentication," Pattern Recognition, vol. 41, no. 10, pp. 31303137, 2008.
TABLE II. AVERAGING DETECTION RESULTS OF PROPOSED SCHEME Items
LSB 29.81 87.61% 0%
Final Detection Results 37523 37976 37523 453 100% 1.21%
[7] A. Khan, S. A. Malik, "A high capacity reversible watermarking approach for authenticating images: Exploiting down-sampling, histogram processing, and block selection," Information Sciences, vol. 256, pp. 162-183, 2014. [8] R. Z. Wang, C. F. Lin, J. C. Lin, "Image hiding by optimal LSB substitution and genetic algorithm," Pattern recognition, vol. 34, no. 3, pp. 671-683, 2001.
To demonstrate the superiority of our proposed method, average comparison results with LSB substitution [8] and Hu et al.’s scheme [13] are shown in Table 3. The results indicate that the PSNR values of the three schemes are for the most part the same (nearly 30 dB). The true detection rate PTD of the proposed scheme and Hu et al.’s scheme were both 100%, which is larger than that of 87.6% in LSB substitution. This indicates that the proposed scheme and Hu et al.’s scheme can accurately detect every modified pixel in the tampered image. With respect to the false detection rate PFD, the proposed scheme achieves better performance with a result of 1.21%, rather than the 5.84% in Hu et al.’s scheme. Thus, after considering the global performance of different schemes, the proposed fragile watermarking scheme is demonstrably superior.
[9] R. Lukac, K. N. Plataniotis, "Color filter arrays: Design and performance analysis," IEEE Transactions on Consumer Electronics, vol. 51, no. 4, pp. 1260-1267, 2005. [10] D. Menon, G. Calvagno, "Color image demosaicking: an overview," Signal Processing Image, vol. 26, no. 8, pp. 518-533, 2011. [11] Y.M. Lu, M. Karzand, M. Vetterli, "Demosaicking by alternating projections: theory and fastone-step implementation," IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 2085-2098, 2010. [12] S. Ferrandas, M. Bertalmio, V. Caselles, "Geometry-based demosaicking," IEEE Transactions on Image Processing, vol. 19, no. 3, pp. 665-670, 2009. [13] Y.C. Hu, W.L. Chen, C.H. Hung, "A novel image authentication technique for color image demosaicking," in Proceedings of 2014 International Conference on Information Technology and Management Engineering, Hong Kong, April, 2014.
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