Abstract
Detection of double JPEG compression plays an increasingly important role in image forensics. This paper mainly focuses on the situation where the images are aligned double JPEG compressed with two different quantization tables. We propose a new detection method based on the fusion features of Benford features and likelihood probability ratio features in this paper. We believe that with the help of likelihood probability ratio features, our fusion features can expose more artifacts left by double JPEG compression, which lead to a better performance. Comparative experiments have been carried out in our paper, and experimental result shows our method outperforms the baseline methods, even when one of the quality factors is pretty high.
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References
Fu, D., Shi, Y.Q., Su, W.: A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: Security, Steganography, and Watermarking of Multimedia Contents IX, pp. 65051L–65051L-11. SPIE, San Jose (2007)
Li, B., Shi, Y.Q., Huang, J.: Detecting doubly compressed JPEG images by using mode based first digit features. In: 10th Workshop on Multimedia Signal Processing, pp. 730–735. IEEE Press, New York (2008)
Feng, X., Doerr, G.: JPEG recompression detection. In: Media Forensics and Security II, pp. 75410J–75410J-12. SPIE, San Jose (2010)
Popescu, A.C., Farid, H.: Statistical tools for digital forensics. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 128–147. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30114-1_10
Popescu, A.C.: Statistical tools for digital image forensics. Ph.D. theses, Department of Computer Science, Dartmouth College, NH (2004)
Prasad S., Ramakrishnan K.R.: On resampling detection and its application to detect image tampering. In: International Conference on Multimedia and Expo, pp. 1325–1328. IEEE Press, New York (2006)
Lukáŝ, J., Fridrich, J.: Estimation of primary quantization matrix in double compressed JPEG images. In: Digital Forensic Research Workshop, pp. 5–8, Cleveland (2003)
Chen, C., Shi, Y.Q., Su, W.: A machine learning based scheme for double JPEG compression detection. In: 19th International Conference on Pattern Recognition, pp. 1–4. IEEE Press, New York (2008)
Shang, S., Zhao, Y., Ni, R.: Double JPEG detection using high order statistic features. In: International Conference on Digital Signal Processing, pp. 550–554. IEEE Press, New York (2016)
Huang, F., Huang, J., Shi, Y.Q.: Detecting double JPEG compression with the same quantization matrix. IEEE Trans. Inf. Forens. Secur. 5(4), 848–856 (2010)
Amerini, I., Becarelli, R., Caldelli, R., Andrea, D.M.: Splicing forgeries localization through the use of first digit features. In: International Workshop on Information Forensics and Security, pp. 143–148. IEEE Press, New York (2014)
Bianchi, T., Piva, A.: Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forens. Secur. 7(3), 1003–1017 (2012)
Bas, P., Filler, T., Pevný, T.: “Break our Steganographic System”: the Ins and Outs of organizing BOSS. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24178-9_5
Dong, L., Kong, X., Wang, B., You, X.: Double compression detection based on Markov model of the first digits of DCT coefficients. In: 6th International Conference on Image and Graphics, pp. 234–237. IEEE Press, New York (2011)
Acknowledgments
This work is supported by the National Science Foundation of China (No. 61502076) and the Scientific Research Project of Liaoning Provincial Education Department (No. L2015114).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yang, F., Li, Y., Chong, K., Wang, B. (2018). Double JPEG Compression Detection Based on Fusion Features. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_16
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DOI: https://doi.org/10.1007/978-3-319-73564-1_16
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