Double JPEG compression forensics based on a convolutional neural network

Eurasip Journal on Information Security

Volumn 2016, Issue 1, 2016, Pages

  Wang, Qing ^a,b   Zhang, Rong ^a,b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Blind image forensics; Classification; Convolutional neural network; Double JPEG compression

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DISCRETE COSINE TRANSFORMS; NEURAL NETWORKS;

AUTOMATIC LOCALIZATION; BLIND IMAGE FORENSICS; CLASSIFICATION RESULTS; COMPRESSION QUALITY; CONVOLUTIONAL NEURAL NETWORK; DETECTION ALGORITHM; DISCRETE COSINE TRANSFORM COEFFICIENTS; DOUBLE JPEG COMPRESSIONS;

IMAGE COMPRESSION;

EID: 84990966225     PISSN: None     EISSN: 2510523X     Source Type: Journal    
DOI: 10.1186/s13635-016-0047-y     Document Type: Article

References (20)

1. J Lukáš, J Fridrich, Estimation of primary quantization matrix in double compressed JPEG images, in Proc. Digital Forensic Research Workshop, 2003, pp. 5–8
2. Popescu A C, Farid H. Statistical tools for digital forensics. International Workshop on Information Hiding. (Springer, Berlin Heidelberg 2004),pp. 128-47.
3. YL Chen, CT Hsu, Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection. IEEE Transactions on Information Forensics and Security 6(2), 396–406 (2011)
4. Fu D, Shi Y Q, Su W. A generalized Benford's law for JPEG coefficients and its applications in image forensics.Electronic Imaging 2007. International Society for Optics and Photonics. 65051L-65051L-11(2007)
5. B Li, YQ Shi, J Huang, Detecting doubly compressed JPEG images by using mode based first digit features, in 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008, pp. 730–735
6. J Fridrich et al., Detection of double-compression in JPEG images for applications in steganography. IEEE Transactions on information forensics and security 3(2), 247–258 (2008)
7. Z Lin, J He, X Tang, CK Tang, Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognition 42(11), 2492–2501 (2009)
8. W Wang, J Dong, T Tan, Exploring DCT coefficient quantization effects for local tampering detection. IEEE Transactions on Information Forensics and Security 9(10), 1653–1666 (2014)
9. Verdoliva L, Cozzolino D, Poggi G. A feature-based approach for image tampering detection and localization. 2014 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE. 149-54(2014)
10. Bianchi T, De Rosa A, Piva A. Improved DCT coefficient analysis for forgery localization in JPEG images. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2444-7(2011)
11. T Bianchi, A Piva, Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Transactions on Information Forensics and Security 7(3), 1003–1017 (2012)
12. Amerini I, Becarelli R, Caldelli R, et al. Splicing forgeries localization through the use of first digit features. 2014 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE. 143-48(2014)
13. Qian Y, Dong J, Wang W, et al. Deep learning for steganalysis via convolutional neural networks. SPIE/IS&T Electronic Imaging. International Society for Optics and Photonics. 94090J-94090J-10(2015)
14. L Pibre, P Jerome, D Ienco, M Chaumont, Deep learning for steganalysis is better than a rich model with an ensemble classifier, and is natively robust to the cover source-mismatch, 2015. arXiv preprint arXiv:1511.04855
15. Y Lecun, B Boser, JS Denker, D Henderson, RE Howard, W Hubbard, LD Jackel, Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4), 541–551 (1989)
16. A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, 2012, pp. 1097–1105
17. GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors. ResearchGate 3(4), 212–223 (2012)
18. Schaefer G, Stich M. UCID: an uncompressed color image database. Electronic Imaging 2004. International Society for Optics and Photonics. 472-80(2003)
19. T Gloe, R Böhme Re, The Dresden Image Database for benchmarking digital image forensics. Journal of Digital Forensic Practice 3(2-4), 150–159 (2010)
20. Y LeCun, Y Bengio, G Hinton, Deep learning. Nature 521(7553), 436–444 (2015)