1. Retaliating Anti-forensics of JPEG Image Compression Based On the Noise Level Estimation PROPOSAL SPRING 2015 ADVISOR: Dr. K.R.Rao Presented by, Komandla Sai Venkat, UTA id: 1001115386 saivenkat.komandla@mavs.uta.edu

2. Acronyms JPEG: The Joint Photographic Experts Group DCT: Discrete Cosine Transform FPR: False positive rates TPR: True positive rates TNR: True negative rates TIFF: Tagged Image File Format

3. TABLE OF CONTENTS  Objective  JPEG compression and its anti-forensics  Anti-forensic dither addition  Summarizing de-blocking theorem  Blocking detection  The noise level detection  Future work  References

4. OBJECTIVE To propose a method to retaliate the anti- forensics of the jpeg compressed image based on the noise level estimation. To estimate the noise level of a particular image and to compare it with a threshold to determine whether it is forged.

5. JPEG compression and its antiforensics  JPEG (Joint Photographic Experts Group ) is very well known ISO/ITU-T standard created in late 1980's.[17].  JPEG compression can be divided into four steps: colour mode conversion and sampling, DCT transform, quantization, and entropy coding.  JPEG compression starts by segmenting an input image into several non overlapping 8 × 8 pixel blocks, then it uses the 2-D DCT to transform each block data into 64 DCT coefficients.

6. Block Diagram of JPEG Compression [16]

7. ANTI-FORENSICS OF JPEG COMPRESSION An anti-forensic method [6] can deceive detectors by first adding anti-forensic dither to DCT transform coefficients to imitate the original uncompressed histograms and then erasing blocking artifacts to remove the compression history by boundary blurring. To erase the compression history, the forger must remove blocking artifact by first median filtering an image and then adding low-power white Gaussian noise to each of its pixel values. In order to detect the forged images, noise level estimation [7] has been employed to estimate the noise added in the deblocking process.

8. Anti-forensic modified process of JPEG compression [6]

9. Anti-forensic dither In order to remove the quantization artifact for a JPEG compressed image, i.e. to make the sub band coefficient value distribution match an original one, the anti forensic dither is added to the DCT coefficient. To hide the compression evidence, [6] dither is introduced into the AC coefficients to approximately restore the histogram of each subband, by: Z = Y + D, where Z is the anti-forensically modified coefficient and D is the additive dither.

10. ORIGINAL IMAGE

11. JPEG compressed image using a quality factor of 90(a), 70 (b), 30 (C), and 10 (d) followed by the addition of anti-forensic dither to its DCT coefficients.

12. THE NOISE DISTRIBUTION The noise distribution for the coefficient Y of zeros value at the (i, j)-th position, is given by:

13. DISTRUBUTION OF ANTIFORENSIC DITHER The distribution of the anti forensic dither added to nonzero quantized DCT coefficients is given by:

14. Quality factor Image quality is the measure of how accurately our image matches the source image which is observed by visible factors like brightness and evenness of illumination, contrast, resolution, geometry, colour fidelity and colour discrimination of an observed image. Most implementations of JPEG compression use a set of quantization matrices indexed by a quality factor from the set {1, 2,..., 100} which are used in the reference implementation provided by the Independent JPEG Group. [19]. A parameter called Q factor IS used to “tune” the quality of the JPEG image which vary from range between 1to 100. A factor 1 produces the image with maximum compression (i.e. smallest) but with worst quality. The factor of 100 produces the image with least compression (i.e. largest) but best quality.

15. JPEG compressed image using a quality factor of 65

16. HISTOGRAMS OF (2, 2) DCT COEFFICIENTS TAKEN FROM AN UNCOMPRESSED VERSION OF THE IMAGE ( A), JPEG COMPRESSION OF THE SAME IMAGE(B)

17. ANTI-FORENSICALLY MODIFIED IMAGE

18. HISTOGRAM OF AN ANTI-FORENSICALLY MODIFIED COPY OF THE JPEG COMPRESSED IMAGE

19. DIFFERENCE WITHIN A BLOCK AND SPANNING ACROSS A BLOCK BOUNDARY HIGHWAY_CIF(352*288)

20. FOR EACH BLOCK (I, J), THE NUMBERS : Z'(i, j) = |A + D - B - C| and Z’’ (i, j) = |E + H - F - G| are computed. By examining the difference between two histograms, the blocking artifacts are detected: Where h 1 is the histogram of Z' each image block, and h 2 is the histogram of Z''. For an uncompressed image, h 1 (1) > h 1 (0) and h 2 (1) > h 2 (0) and for a JPEG compressed image, h 1 (1) > h 1 (0) and h 2 (1) > h 2 (0) may not meet or not meet at the same time.

21. SUMMARY OF THE DE-BLOCKING ALGORITHM Where u i,j represents the pixel value at location (i,j) in an unmodified image, and v i,j denotes its de-blocked counterpart, med s ( ) denotes a two-dimensional median filter with a square window of size s pixels, and n i,j is a zero mean Gaussian random noise with variance.

22. BLOCKING ARTIFACT DETECTION ACCURACY RESULTS Results of blocking artifact detection accuracy from experiments by varying the window size(s) and the variance σ for FPR (False positive rates) varies in a given interval and the optimal threshold to obtain the Accuracy Rate = (TPR + TNR) / 2. TPR is true positive rates and TNR is true negative rates. The results show that the above method can remove the blocking artifact effectively.

23. THE NOISE LEVEL ESTIMATION

24. PERFORMANCE OF THE NOISE LEVEL ESTIMATION USING THE TWO METHODS

25. SUMMARY ON NOISE LEVEL ESTIMATION The noise level estimation can be summarized as the following major steps: 1. Decomposing the test image into overlapping patches. The default patch size is 7 × 7 pixels. 2. Estimating an initial noise level σ e from the covariance matrix as : Where Σ y' is the covariance matrix of the selected patches and is the minimum Eigen value of Σ y'. 3. Selecting the weak textured patches from the test image using a threshold that varies with σ e.. 4. Estimating a new noise level σ e using the selected patches. The process of step 3 and 4 is iterated until σ e is stable. Here σ e is used to denote the estimated noise level of a test image.

26. REFERENCES [1] J. Lukas and J. Fridrich, “Estimation of primary quantization matrix in double compressed JPEG images,” in Proc. of SPIE 6819,Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, pp. 681911, February 26, 2008. [2] T. Pevny and J. Fridrich, “Detection of double-compression in JPEG images for applications in steganography,” IEEE Transactions on Information Forensics and Security, vol. 3,no.2, pp. 247-258, June 2008. [3] W. Luo, Z. Qu and J. Huang,G. Qiu, “A novel method for detecting cropped and recompressed image block,” in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing 2007 (ICASSP 2007), pp. II-217-II-220, Feb. 2003. [4] Z. Fan and R. L. de Queiroz “Identification of bitmap compression history: JPEG detection and quantizer estimation,” IEEE Transactions on Image Processing, vol.12, no. 2, pp. 230-235,Feb. 2003. [5] D. Fu, Y. Q. Shi, W. Su, “A generalized Benford’s law for JPEG coefficients and its applications in image forensics,” In Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6505,Security, Steganography, and Watermarking of Multimedia Contents, pp. 65051L-165051L-11, Jan. 2007, San Jose, CA,USA. [1] J. Lukas and J. Fridrich, “Estimation of primary quantization matrix in double compressed JPEG images,” in Proc. of SPIE 6819,Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, pp. 681911, February 26, 2008. [2] T. Pevny and J. Fridrich, “Detection of double-compression in JPEG images for applications in steganography,” IEEE Transactions on Information Forensics and Security, vol. 3,no.2, pp. 247-258, June 2008. [3] W. Luo, Z. Qu and J. Huang,G. Qiu, “A novel method for detecting cropped and recompressed image block,” in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing 2007 (ICASSP 2007), pp. II-217-II-220, Feb. 2003. [4] Z. Fan and R. L. de Queiroz “Identification of bitmap compression history: JPEG detection and quantizer estimation,” IEEE Transactions on Image Processing, vol.12, no. 2, pp. 230-235,Feb. 2003. [5] D. Fu, Y. Q. Shi, W. Su, “A generalized Benford’s law for JPEG coefficients and its applications in image forensics,” In Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6505,Security, Steganography, and Watermarking of Multimedia Contents, pp. 65051L-165051L-11, Jan. 2007, San Jose, CA,USA.

27. Future Work Using the de-blocking algorithm, noise level estimation method is proposed to retaliate the anti forensic method. In order to forge an image, the forger must add the Gaussian noise to erase the blocking artifacts. That is, forged images may present a higher noise level than original images. A reasonable variance of the noise is selected to cover the tampering trace and not to introduce new artifacts; Blocking detection and noise level detection are used to evaluate the forged image.

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