S. Tan and B. Li, Stacked convolutional auto-encoders for steganalysis of digital images, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, pp.1-4, 2014.
DOI : 10.1109/APSIPA.2014.7041565

Y. Qian, J. Dong, W. Wang, and T. Tan, Deep Learning for Steganalysis via Convolutional Neural Networks, Proceedings of Media Watermarking Part of IS&T/SPIE Annual Symposium on Electronic Imaging, SPIE'2015, pp.94090-94090, 2015.
DOI : 10.1117/12.2083479

L. Pibre, J. Pasquet, D. Ienco, and M. Chaumont, Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch, Proceedings of Media Watermarking, Security, and Forensics, MWSF'2016, Part of I&ST International Symposium on Electronic Imaging, EI'2016, pp.1-11, 2016.
DOI : 10.2352/ISSN.2470-1173.2016.8.MWSF-078

URL : https://hal.archives-ouvertes.fr/lirmm-01227950

G. Xu, H. Wu, and Y. Q. Shi, Ensemble of CNNs for Steganalysis, Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec '16, pp.103-107, 2016.
DOI : 10.1186/1687-417X-2014-1

G. Xu, H. Z. Wu, and Y. Q. Shi, Structural Design of Convolutional Neural Networks for Steganalysis, IEEE Signal Processing Letters, vol.23, issue.5, pp.708-712, 2016.
DOI : 10.1109/LSP.2016.2548421

J. Zeng, S. Tan, B. Li, and J. Huang, Pre-training via fitting deep neural network to rich-model features extraction procedure and its effect on deep learning for steganalysis, Proceedings of Media Watermarking, Security, and Forensics 2017, MWSF'2017, Part of IS&T Symposium on Electronic Imaging, EI'2017, p.6, 2017.
DOI : 10.2352/ISSN.2470-1173.2017.7.MWSF-324

M. Chen, V. Sedighi, M. Boroumand, and J. Fridrich, JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images, Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security , IHMMSec '17, pp.17-75, 2017.
DOI : 10.1145/952532.952595

G. Xu, Deep Convolutional Neural Network to Detect J-UNIWARD, Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security , IHMMSec '17, pp.17-67, 2017.
DOI : 10.1007/978-3-319-46493-0_38

URL : http://arxiv.org/pdf/1704.08378

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.
DOI : 10.1109/CVPR.2016.90

URL : http://arxiv.org/pdf/1512.03385

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

J. Kodovsk´ykodovsk´y, J. Fridrich, and V. Holub, Ensemble Classifiers for Steganalysis of Digital Media, IEEE Transactions on Information Forensics and Security, vol.7, issue.2, pp.432-444, 2012.
DOI : 10.1109/TIFS.2011.2175919

J. Fridrich and J. Kodovsk´ykodovsk´y, Rich Models for Steganalysis of Digital Images, IEEE Transactions on Information Forensics and Security, vol.7, issue.3, pp.868-882, 2012.
DOI : 10.1109/TIFS.2012.2190402

C. Xia, Q. Guan, X. Zhao, Z. Xu, and Y. Ma, Improving GFR Steganalysis Features by Using Gabor Symmetry and Weighted Histograms, Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security , IHMMSec '17, pp.17-28, 2017.
DOI : 10.1007/s11554-016-0600-4

T. Denemark, V. Sedighi, V. Holub, R. Cogranne, and J. Fridrich, Selection-channel-aware rich model for Steganalysis of digital images, 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp.48-53, 2014.
DOI : 10.1109/WIFS.2014.7084302

T. Denemark, M. Boroumand, J. Fridrich-]-y, J. Qian, W. Dong et al., Steganalysis Features for Content-Adaptive JPEG Steganography, Proceedings of IEEE International Conference on Image Processing, pp.1736-17462016, 2016.
DOI : 10.1109/TIFS.2016.2555281

J. Ye, J. Ni, and Y. Yi, Deep Learning Hierarchical Representations for Image Steganalysis, IEEE Transactions on Information Forensics and Security, vol.12, issue.11, pp.2545-2557, 2017.
DOI : 10.1109/TIFS.2017.2710946

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning , ICML 2015, pp.6-11, 2015.

M. Lin, Q. Chen, and S. Yan, Network in network, International Conference on Learning Representations, ICLR 2014, p.10, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01551350

V. Holub, J. Fridrich, and T. Denemark, Universal distortion function for steganography in an arbitrary domain, EURASIP Journal on Information Security, vol.5, issue.2, 2014.
DOI : 10.1007/978-3-642-55760-6

V. Holub and J. Fridrich, Designing steganographic distortion using directional filters, 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp.234-239, 2012.
DOI : 10.1109/WIFS.2012.6412655

URL : http://dde.binghamton.edu/vholub/pdf/WIFS12_Designing_Steganographic_Distortion_Using_Directional_Filters.pdf

P. Bas, T. Filler, and T. Pevn´ypevn´y, ???Break Our Steganographic System???: The Ins and Outs of Organizing BOSS, Proceedings of the 13th International Conference on Information Hiding, pp.59-70, 2011.
DOI : 10.1007/978-3-642-16435-4_13

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.675-678, 2014.
DOI : 10.1145/2647868.2654889

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS'2010 of Proceedings of Machine Learning Research, pp.249-256, 2010.

P. Bas and T. Furon, BOWS-2 Contest (Break Our Watermarking System) Organized between the 17th of, 2007.

M. Yedroudj, M. Chaumont, and F. Comby, How to augment a small learning set for improving the performances of a CNNbased steganalyzer?, Proceedings of Media Watermarking, Security, and Forensics, MWSF'2018, Part of IS&T International Symposium on Electronic Imaging, 2002.
URL : https://hal.archives-ouvertes.fr/lirmm-01681883