Deep learning for steganalysis via convolutional neural networks

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 9409, Issue , 2015, Pages

  Qian, Yinlong ^a,b   Dong, Jing ^b   Wang, Wei ^b   Tan, Tieniu ^b  

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

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

Convolutional Neural Networks; Deep Learning; Feature Learning; Gaussian Nonlinearity; Steganalysis

Indexed keywords

COMPLEX NETWORKS; CONVOLUTION; EXTRACTION; FEATURE EXTRACTION; NEURAL NETWORKS; STEGANOGRAPHY;

CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; FEATURE LEARNING; GAUSSIAN NONLINEARITY; STEGANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 84932146885     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2083479     Document Type: Conference Paper

References (25)

1. Pevny, T., Bas, P., and Fridrich, J., "Steganalysis by subtractive pixel adjacency matrix," IEEE Transactions on information Forensics and Security 5(2), 215-224 (2010).
2. Fridrich, J. and Kodovsky, J., "Rich models for steganalysis of digital images," IEEE Transactions on Information Forensics and Security 7(3), 868-882 (2012).
3. Shi, Y. Q., Sutthiwan, P., and Chen, L., "Textural features for steganalysis," in [Information Hiding], 63-77, Springer (2013).
4. Holub, V., Fridrich, J., and Denemark, T., "Random projections of residuals as an alternative to cooccurrences in steganalysis," in [IS&T/SPIE Electronic Imaging], 86650L-86650L, International Society for Optics and Photonics (2013).
5. Holub, V. and Fridrich, J., "Random projections of residuals for digital image steganalysis," IEEE Trans-actions on Information Forensics and Security 8(12), 1996-2006 (2013).
6. Hinton, G. E. and Salakhutdinov, R. R., "Reducing the dimensionality of data with neural networks," Science 313(5786), 504-507 (2006).
7. Salakhutdinov, R. and Hinton, G. E., "Deep boltzmann machines," in [International Conference on artificial Intelligence and Statistics], 448-455 (2009).
8. Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P., "Exploring strategies for training deep neural networks," The Journal of Machine Learning Research10, 1-40 (2009).
9. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., "Gradient-based learning applied to document recognition," Proceedings of the IEEE86(11), 2278-2324 (1998).
10. Hinton, G., Osindero, S., and Teh, Y.-W., "A fast learning algorithm for deep belief nets," Neural compu-tation18(7), 1527-1554 (2006).
11. Ranzato, M., Huang, F. J., Boureau, Y.-L., and LeCun, Y., "Unsupervised learning of invariant feature hierarchies with applications to object recognition," in [IEEE Conference on Computer Vision and Pattern Recognition], 1-8, IEEE (2007).
12. Collobert, R. and Weston, J., "A unified architecture for natural language processing: Deep neural networks with multitask learning," in [Proceedings of the 25th international conference on Machine learning], 160-167, ACM (2008).
13. Krizhevsky, A., Sutskever, I., and Hinton, G. E., "Imagenet classification with deep convolutional neural networks," in [Advances in neural information processing systems], 1097-1105 (2012).
14. Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., and Schmidhuber, J., "Flexible, high performance convolutional neural networks for image classification," in [IJCAI Proceedings-International Joint Conference on artificial Intelligence], 22(1), 1237 (2011).
15. Browne, M. and Ghidary, S. S., "Convolutional neural networks for image processing: an application in robot vision," in [AI 2003: Advances in artificial Intelligence], 641-652, Springer (2003).
16. Bishop, C. M. et al., [Pattern recognition and machine learning], vol. 1, springer New York (2006).
17. Boureau, Y.-L., Ponce, J., and LeCun, Y., "A theoretical analysis of feature pooling in visual recognition," in [Proceedings of the 27th International Conference on Machine Learning (ICML-10)], 111-118 (2010).
18. Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y., "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in [Proceedings of the 26th Annual International Conference on Machine Learning], 609-616, ACM (2009).
19. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R., "Improving neural networks by preventing co-adaptation of feature detectors," arXiv preprint arXiv:1207.0580(2012).
20. Nair, V. and Hinton, G. E., "Rectified linear units improve restricted boltzmann machines," in [Proceedings of the 27th International Conference on Machine Learning (ICML-10)], 807-814 (2010).
21. Krizhevsky, A., "cuda-convnet," (2012). http://code.google.com/p/cuda-convnet/.
22. Bas, P., Filler, T., and Pevny, T., " break our steganographic system: The ins and outs of organizing boss," in [Information Hiding], 59-70, Springer (2011).
23. Pevny, T., Filler, T., and Bas, P., "Using high-dimensional image models to perform highly undetectable steganography," in [Information hiding], 161-177, Springer (2010).
24. Holub, V. and Fridrich, J., "Designing steganographic distortion using directional filters.," in [The IEEE International Workshop on Information Forensics and Security (WIFS)], 234-239 (2012).
25. Holub, V. and Fridrich, J., "Digital image steganography using universal distortion," in [Proceedings of the first ACM workshop on Information hiding and multimedia security], 59-68, ACM (2013).