J. Hayes and G. Danezis, Generating Steganographic Images Via Adversarial Training, Proceedings ofAdvances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.1951-1960, 2017.

J. Zhu, R. Kaplan, J. Johnson, and L. Fei-fei, HiDDeN: Hiding Data With Deep Networks, Proceedings of the 15th European Conference on Computer Vision, ECCV, vol.11219, pp.682-697, 2018.

G. J. Simmons, The Subliminal Channel and Digital Dignatures, Proceeding of Crypto'83, E. by D. Chaum, pp.51-67, 1983.

A. D. Ker, P. Bas, R. Böhme, R. Cogranne, S. Craver et al., Moving Steganography and Steganalysis from the Laboratory into the Real World, Proceedings of the 1st ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec'2013, pp.45-58, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00836407

A. Kerckhoffs, La Cryptographie Militaire, Journal des Sciences Militaires, vol.IX, pp.161-191, 1883.

V. Holub, J. Fridrich, and T. Denemark, Universal Distortion Function for Steganography in an Arbitrary Domain, EURASIP Journal on Information Security, vol.2014, issue.1, 2014.

V. Holub and J. Fridrich, Designing Steganographic Distortion Using Directional Filters, Proceedings of the IEEE International Workshop on Information Forensics and Security, WIFS'2012, pp.234-239, 2012.

V. Sedighi, R. Cogranne, and J. Fridrich, Content-Adaptive Steganography by Minimizing Statistical Detectability, IEEE Transactions on Information Forensics and Security, vol.11, issue.2, pp.221-234, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01906608

P. Schöttle and R. Böhme, A Game-theoretic Approach to Content-adaptive Steganography, Proceedings of the 14th International Conference on Information Hiding, ser. IH'12, pp.125-141, 2012.

, Game Theory and Adaptive Steganography, vol.11, pp.760-773, 2016.

M. Chaumont, Deep Learning in steganography and steganalysis, Digital Media Steganography: Principles, Algorithms, Advances, M. Hassaballah, pp.321-349, 2020.
URL : https://hal.archives-ouvertes.fr/lirmm-02087729

J. Kodovsky, J. Fridrich, and V. Holub, On Dangers of Overtraining Steganography to Incomplete Cover Model, Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, ser. MM&MMSec'2011, pp.69-76, 2011.

S. Kouider, M. Chaumont, and W. Puech, Adaptive Steganography by Oracle (ASO), Proceedings of the IEEE International Conference on Multimedia and Expo, ICME'2013, pp.1-6, 2013.
URL : https://hal.archives-ouvertes.fr/lirmm-00838993

M. Abadi and D. G. Andersen, Learning to Protect Communications with Adversarial Neural Cryptography, ArXiv; Rejected from the 5th International Conference on Learning Representations, 2016.

D. Hu, L. Wang, W. Jiang, S. Zheng, and B. Li, A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks, IEEE Access, vol.6, pp.38-303, 2018.

W. Tang, S. Tan, B. Li, and J. Huang, Automatic Steganographic Distortion Learning Using a Generative Adversarial Network, IEEE Signal Processing Letters, vol.24, issue.10, pp.1547-1551, 2017.

W. Tang, B. Li, S. Tan, M. Barni, and J. Huang, CNN-based Adversarial Embedding for Image Steganography, IEEE Transactions on Information Forensics and Security, pp.1-1, 2019.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, Rectifier Nonlinearities Improve Neural Network Acoustic Models, Proceedings of ICML Workshop on Deep Learning for Audio, Speech and Language Processing, 2013.

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 Source-Mismatch, Proceedings of Media Watermarking, Security, and Forensics, MWSF'2016, Part of I&ST International Symposium on Electronic Imaging, EI'2016, pp.1-11, 2016.
URL : https://hal.archives-ouvertes.fr/lirmm-01227950

J. Fridrich, Steganography in Digital Media, 2009.

T. Taburet, P. Bas, J. Fridrich, and W. Sawaya, Computing Dependencies between DCT Coefficients for Natural Steganography in JPEG Domain, Proceedings of the 7th ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec'2019, pp.57-62, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02165866

D. Lerch-hostalot and D. Megìas, Unsupervised Steganalysis Based on Artificial Training Sets, Engineering Applications of Artificial Intelligence, vol.50, pp.45-59, 2016.

J. Kodovský, J. Fridrich, and V. Holub, Ensemble Classifiers for Steganalysis of Digital Media, IEEE Transactions on Information Forensics and Security, TIFS, vol.7, issue.2, pp.432-444, 2012.

J. Fridrich and J. Kodovský, Rich Models for Steganalysis of Digital Images, IEEE Transactions on Information Forensics and Security, TIFS, vol.7, issue.3, pp.868-882, 2012.

M. Yedroudj, F. Comby, and M. Chaumont, Yedroudj-Net: An Efficient CNN for Spatial Steganalysis, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2092-2096, 2018.

B. Li, W. Wei, A. Ferreira, and S. Tan, ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis, IEEE Signal Processing Letters, vol.25, issue.5, pp.650-654, 2018.

M. Boroumand, M. Chen, and J. Fridrich, Deep Residual Network for Steganalysis of Digital Images, IEEE Transactions on Information Forensics and Security, pp.1-1, 2018.

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.
URL : https://hal.archives-ouvertes.fr/lirmm-01681883

R. Zhang, F. Zhu, J. Liu, and G. Liu, Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNNbased Steganalysis (previously named "efficient feature learning and multi-size image steganalysis based on cnn, IEEE Transactions on Information Forensics and Security, TIFS, vol.15, pp.1138-1150, 2020.

T. Filler, J. Judas, and J. Fridrich, Minimizing Embedding Impact in Steganography Using Trellis-Coded Quantization, Proceedings of IS&T/SPIE Annual Symposium on Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents

J. Edward, I. Delp, P. W. Wong, and . Spie', , vol.7541, pp.1-14, 2010.

O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention, MICCAI'2015, ser. LNCS, vol.9351, pp.234-241, 2015.

P. Bas, T. Filler, and T. Pevný, Break Our Steganographic System': The Ins and Outs of Organizing BOSS, Proceedings of the 13th International Conference on Information Hiding, IH'2011, ser, vol.6958, pp.59-70, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00648057

P. Bas and T. Furon, BOWS-2 Contest (Break Our Watermarking System)," organised within the activity of the Watermarking Virtual Laboratory (Wavila) of the European Network of Excellence ECRYPT, 2008, organized between the 17th of, and the 17th of, 2007.

X. Deng, B. Chen, W. Luo, and D. Luo, Fast and Effective Global Covariance Pooling Network for Image Steganalysis, Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, ser. IH&MMSec'2019, pp.230-234, 2019.

M. Yedroudj, M. Chaumont, F. Comby, A. Oulad-amara, and P. Bas, Pixels-off: Data-augmentation Complemen-tary Solution for Deep-learning Steganalysis, Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, ser. IH&MMSec'2020, 2020.