Abstract
In this paper, we point out state-of-the-art algorithm in natural image splicing detection, namely the transition probability matrix feature proposed by Shi, et al., can be attacked by modifying block discrete cosine transform (BDCT) coefficients without significantly degrading quality of the spliced image. BDCT coefficients of the spliced image are modified so that its distance to a close authentic image in feature space is minimized. The minimization is accomplished with a greedy algorithm. The modification makes the spliced image statistically similar to the authentic image so as to reduce the effectiveness of detection algorithm. The performance of the algorithm is evaluated on Columbia Image Splicing Detection Evaluation Dataset. With the proposed anti-forensics post processing, detection accuracy and true positive rate reduces to 69.4% and 62.5% respectively, while the processed images still maintain average peak signal-to-noise ratio (PSNR) at 42.22db.
This work is supported by National Science Foundation of China (61271316, 61071152), 973 Program of China (2013CB329605) and Chinese National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAH38B04).
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References
Pevny, T., Fridrich, J.: Detection of Double-Compression in JPEG Images for Applications in Steganography. IEEE Transactions on Information Forensics and Security 3(2) (2008)
Fridrich, J., Soukal, D., Lukáš, J.: Detection of copy-move forgery in digital images. In: Digital Forensic Research Workshop (2003)
Ryu, S.-J., Lee, M.-J., Lee, H.-K.: Detection of copy-rotate-move forgery using zernike moments. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 51–65. Springer, Heidelberg (2010)
Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on Signal Processing 53(2), 758–767 (2005)
Shi, Y.Q., Chen, C., Chen, W.: A Natural Image Model Approach to Splicing Detection. In: The 9th workshop on Multimedia & Security, pp. 51–62. ACM, New York (2007)
Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: The 7th workshop on Multimedia and Security. ACM (2005)
Kirchner, M., Böhme, R.: Hiding Traces of Resampling in Digital Images. IEEE Transactions on Information Forensics and Security 3(4) (2008)
Kirchner, M., Bohme, R.: Synthesis of color filter array pattern in digital images. In: Proc. of SPIE. SPIE, San Jose (2009)
Cao, G., et al.: Anti-Forensics of Contrast Enhancement in Digital Images. In: MM&Sec 2010. ACM, Rome (2010)
Stamm, M.C., et al.: Anti-Forensics of Jpeg Compression. In: ICASSP. IEEE (2010)
Shi, Y.Q., Chen, C.-H., Xuan, G., Su, W.: Steganalysis versus splicing detection. In: Shi, Y.Q., Kim, H.-J., Katzenbeisser, S. (eds.) IWDW 2007. LNCS, vol. 5041, pp. 158–172. Springer, Heidelberg (2008)
Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 161–177. Springer, Heidelberg (2010)
Ng, T.-T., Chang, S.-F.: A Dataset of Authentic and Spliced Image Blocks, ADVENT Technical Report, #203-2004-3, Columbia University (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3) (2011)
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Chen, L., Wang, S., Li, S., Li, J. (2013). Countering Universal Image Tampering Detection with Histogram Restoration. In: Shi, Y.Q., Kim, HJ., Pérez-González, F. (eds) The International Workshop on Digital Forensics and Watermarking 2012. IWDW 2012. Lecture Notes in Computer Science, vol 7809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40099-5_23
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DOI: https://doi.org/10.1007/978-3-642-40099-5_23
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