A deep learning approach to universal image manipulation detection using a new convolutional layer

IH and MMSec 2016 - Proceedings of the 2016 ACM Information Hiding and Multimedia Security Workshop

Volumn , Issue , 2016, Pages 5-10

  Bayar, Belhassen ^a   Stamm, Matthew C ^a  

^a DREXEL UNIVERSITY   (United States)

Author keywords

Convolutional neural networks; Image forensics; Universal forgery detection

Indexed keywords

CONVOLUTION; DEEP LEARNING; FORENSIC ENGINEERING; NETWORK ARCHITECTURE; NEURAL NETWORKS;

CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; DETECTION FEATURES; IMAGE FORENSICS; IMAGE MANIPULATION; LEARNING APPROACH; MULTIPLE IMAGE; UNIVERSAL FORGERY;

FEATURE EXTRACTION;

EID: 85015106454     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2909827.2930786     Document Type: Conference Paper

References (21)

1. C. Chen and M. C. Stamm. Camera model identification framework using an ensemble of demosaicing features. In Information Forensics and Security (WIFS), 2015 IEEE International Workshop on, pages 1-6. IEEE, 2015.
2. J. Chen, X. Kang, Y. Liu, and Z. J. Wang. Median filtering forensics based on convolutional neural networks. IEEE Signal Processing Letters, 22(11):1849-1853, Nov. 2015.
3. W. Fan, K. Wang, and F. Cayre. General-purpose image forensics using patch likelihood under image statistical models. In IEEE International Workshop on Information Forensics and Security (WIFS), pages 1-6, Nov. 2015.
4. J. Fridrich and J. Kodovsky. Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 7(3):868-882, 2012.
5. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. Improving neural networks by preventing co-Adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
6. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.
7. X. Kang, M. C. Stamm, A. Peng, and K. J. R. Liu. Robust median filtering forensics using an autoregressive model. IEEE Transactions on Information Forensics and Security, 8(9):1456-1468, Sept. 2013.
8. M. Kirchner. Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. In Proceedings of the 10th ACM Workshop on Multimedia and Security, MM&Sec '08, pages 11-20, New York, NY, USA, 2008. ACM.
9. M. Kirchner and J. Fridrich. On detection of median filtering in digital images. In IS&T/SPIE Electronic Imaging, pages 754110-754110. International Society for Optics and Photonics, 2010.
10. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
11. B. B. Le Cun, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems. Citeseer, 1990.
12. Y. Le Cun and Y. Bengio. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10): 1995, .
13. Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541-551, 1989.
14. Y. A. Le Cun, L. Bottou, G. B. Orr, and K.-R. Müller. Efficient backprop. In Neural networks: Tricks of the trade, pages 9-48. Springer, 2012.
15. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In International Conference on Machine Learning, pages 807-814, 2010.
16. T. Pevny, P. Bas, and J. Fridrich. Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on Information Forensics and Security, 5(2):215-224, June 2010.
17. A. C. Popescu and H. Farid. Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on Signal Processing, 53(2):758-767, Feb. 2005.
18. X. Qiu, H. Li, W. Luo, and J. Huang. A universal image forensic strategy based on steganalytic model. In Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pages 165-170. ACM, 2014.
19. M. C. Stamm and K. J. R. Liu. Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans. on Information Forensics and Security, 5(3):492 -506, 2010.
20. M. C. Stamm, M. Wu, and K. J. R. Liu. Information forensics: An overview of the first decade. IEEE Access, 1:167-200, 2013.
21. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.