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A passive-blind image forgery detection based on JPEG quantization
|關鍵字:||JPEG image;JPEG 影像;Fourier Transform;Quantization table estimation;Forgery detection;傅立葉轉換;量化表估測;竄改影像偵測||出版社:||電機工程學系所||引用:||C.-S. Lu and H.-Y. Mark Liao, “Multipurpose Watermarking for Image Authentication and Protection,” IEEE Trans. Image Processing, vol. 10, no. 10, pp. 1579-1592, 2001. W.-N. Lie, G.-S. Lin, and S.-L. Cheng, “Dual protection of JPEG images based on informed embedding and two-stage watermark extraction techniques,” IEEE Trans. Information Forensics and Security, vol. 1, no. 3, pp.330-341, 2006. S. Ye, Q. Sun and E.-C. Chang, “Detecting digital image forgeries by measuring inconsistencies of blocking artifact,” in Proc. IEEE International Conference on Multimedia & Expo, pp. 12-15, 2007. C.-C. Hsu, T.-Y. Hung, C.-W. Lin, and C.-T. Hsu, “Video forgery detection using sensor pattern noise,” 2008 21th Conference on Computer Vision, Graphics and Image Processing, Taiwan, R.O.C. W. Luo, Z. Qu, J. Huang, and G. Qiu, “A novel method for detection cropped and recompressed image block,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. II-217-II-220, 2007. T. V. Lanh, K.-S. Chong, S. Emmanuel, and M. S Kankanhalli, “A survey on digital camera image forensic methods,” in Proc. IEEE International Conference on Multimedia & Expo, pp. 16-19, 2007. Jin-Bing Huang, Edge point detection and texture analysis for image inpainting, Master Thesis, National Chung Hsing University, 2006. Teorex, http://www.teorex.com/inpaint.html J. Fridrich, M. Goljan, and R. Du, “Steganalysis based on JPEG compatibility,” in Proc. SPIE Multimedia Systems and Applications IV, pp.275-280, 2001. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, 2001. H. V. Poor, An introduction to Signal Detection and Estimation, Springer, 1994. Y.-F. Hsu and S.-F. Chang, “Image splicing detection using camera response function consistency and automatic segmentation,” in Proc. IEEE Conf. Multimedia Expo., pp. 28-31, July 2007. A.C. Popescu and H. Farid, “Exposing digital forgeries in color filter array interpolated images,” IEEE Trans. Signal Processing, vol. 53, no.10, pp. 3948-3959, Oct. 2005. S. Bayram, H. T. Sencar, and N. Memon, “Source camera identification based on CFA interpolation,” in Proc. IEEE Int. Conf. Image Processing, vol. 3, pp. III-69-72, 2006. J. Lukas, J. Fridrich, and M. Goljan, “Digital camera identification from sensor pattern noise,” IEEE Trans. Information Forensics Security, vol. 1, no. 2, pp. 205-214, 2006. M. Chen, J. Fridrich, and J. Lukas, “Determining image origin and integrity using sensor pattern noise,” IEEE Trans. Information Forensics Security, vol. 3, no. 1, pp. 74-90, 2008. A. Swaminathan, M. Wu, and K. J. R. Liu, “Digital image forensics via intrinsic fingerprints,” IEEE Trans. Information Forensics Security, vol. 3, no. 1, pp. 101-117, 2008. Y. L. Chen and C.-T. Hsu, “Image tampering detection by blocking periodicity analysis in JPEG compressed images,” in Proc IEEE 10th Workshop on Multimedia Signal Processing, pp. 803-808, 2008. I. Avcibas, S. Bayram, N. Memon, B. Sankur, and M. Ramkumar, “A Classifier Design for Detecting Image Manipulations,” in Proc. IEEE Int. Conf. Image Processing, vol. 4, pp. 24-27, 2004. H. Farid, “A Survey of Image Forgery Detection,” IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16-25,2009. H. Farid, “Exposing digital forgeries from JPEG ghosts,” IEEE Trans. Information Forensics and Security, vol. 4, no. 1, pp.154-160, 2009.||摘要:||
由於數位浮水印本身的限制問題，本論文並不採取加入浮水印的方法來保護圖片。本論文從JPEG 壓縮的觀點，來討論圖片被竄改處與未被竄改處有何不同，設法偵測出圖片遭受竄改處。在偵測過程中，首先測試影像是否曾經遭受JPEG壓縮過。本論文將討論如何估測JPEG 壓縮時所用的量化表，並衡量與估測方法的優劣比較。若是測試影像被判斷曾經被JPEG 壓縮過，再進一步設法找出影像被竄改處。為了估測出正確的量化表，必須盡可能地將竄改的區域加以排除後再進行量化表的估測。本論文設計演算法盡可能的選出影像未被竄改的部份再加以估測。待估測完畢，則量化表將用來計算整張影像中每個8x8區塊的量化誤差。最後，所有量化誤差經由一個MLR 分類器分類是否屬於被竄改的區塊後，便可找出影像被竄改的位置。由實驗結果顯示，本論文設計的演算法確實可以正確的找出圖片被竄改的位置。
In this thesis, we proposed a passive scheme to achieve image forgery. The inconsistency measure of quantization table is characterized to develop the proposed scheme. To raise the accuracy of quantization table estimation, each AC DCT coefficient is first classified into different types and then the corresponding quantization stepsize is adaptively measured from its power spectrum density (PSD) and PSD's Fourier transform. Based on the content-adaptive quantization table estimation, the proposed scheme is composed of pre-screening, candidate region selection, and tampering region identification. To decide whether an input image had been JPEG compressed, the number that the quantization stepsize is one is measured in the pre-screening. To select candidate regions for estimating quantization table, we devise the algorithm for candidate region selection consisting of seed region generation and region growing. The seed region generation is first used to find a suitable region by removing suspicious tampered regions. Based on the seed region, the candidate region can be obtained by suitably merging other regions into the seed. To avoid merging the suspect regions, a candidate region refinement is performed in the region growing. After estimating the quantization table from the candidate region, a MLR classifier based on the inconsistency of quantization table is exploited to identify tampered regions block by block. The experimental results demonstrate that our proposed scheme can not only estimate the quantization table but also identify tampered regions well.
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