P. Bas, T. Filler, and T. Pevný, Break our steganographic system -the ins and outs of organizing boss, Proc. Information Hiding, pp.59-70, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00648057

Y. Bengio, A. C. Courville, and P. Vincent, Representation Learning : A Review and New Perspectives, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.PAMI, issue.8, pp.1798-1828, 2013.

M. Boroumand, M. Chen, and J. Fridrich, Deep Residual Network for Steganalysis of Digital Images, IEEE Trans. on Information Forensics and Security, vol.14, issue.5, pp.1181-1193, 2019.

J. Butora and J. Fridrich, Reverse jpeg compatibility attack (available as Early Access), pp.1-1, 2019.

M. Chen, V. Sedighi, M. Boroumand, and J. Fridrich, JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images, Proc. ACM Workshop on Information Hiding and Multimedia Security, pp.75-84, 2017.

R. Cogranne, Détection statistique d'informations cachées dans une image naturelle à partir d'un modèle physique, 2011.

R. Cogranne, Selection-channel-aware reverse jpeg compatibility for highly reliable steganalysis of jpeg images, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02460091

R. Cogranne and J. Fridrich, Modeling and extending the ensemble classifier for steganalysis of digital images using hypothesis testing theory, Information Forensics and Security, IEEE Trans. on, 2015.

R. Cogranne, Q. Giboulot, and P. Bas, The alaska steganalysis challenge : A first step towards steganalysis into the wild, Proc. ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec'19, pp.125-137, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02147763

R. Cogranne, V. Sedighi, and J. Fridrich, Practical strategies for content-adaptive batch steganography and pooled steganalysis, 2017 IEEE Intl' Conference on Acoustics, Speech and Signal Processing, pp.2122-2126, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01915645

R. Cogranne, V. Sedighi, J. Fridrich, and T. Pevný, Is ensemble classifier needed for steganalysis in high-dimensional feature spaces ?, in Information Forensics and Security (WIFS), IEEE 7th Intl' Workshop on, pp.1-6, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02407684

T. Denemark, M. Boroumand, and J. Fridrich, Steganalysis Features for Content-Adaptive JPEG Steganography, vol.11, pp.1736-1746, 2016.

T. Denemark, V. Sedighi, V. Holub, R. Cogranne, and J. Fridrich, Selectionchannel-aware rich model for steganalysis of digital images, Information Forensics and Security (WIFS), pp.48-53, 2014.

J. Fridrich and M. Goljan, On estimation of secret message length in LSB steganography in spatial domain, Security, Steganography, and Watermarking of Multimedia Contents VI, vol.5306, pp.23-34, 2004.

J. Fridrich and J. Kodovsk?, Rich models for steganalysis of digital images, formation Forensics and Security, vol.7, pp.868-882, 2012.

C. Fuji-tsang and J. J. Fridrich, Steganalyzing Images of Arbitrary Size with CNNs, Proc. IS&T Electronic Imaging, p.121, 2018.

Q. Giboulot, R. Cogranne, D. Borghys, and P. Bas, Effects and solutions of cover-source mismatch in image steganalysis, 2020.

M. Goljan and J. Fridrich, Cfa-aware features for steganalysis of color images, Media Watermarking, Security, and Forensics, vol.9409, p.94090, 2015.

M. Goljan, J. Fridrich, and R. Cogranne, Rich model for steganalysis of color images, 2014 IEEE Intl' Workshop on Information Forensics and Security (WIFS), pp.185-190, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, Proc. of the European Conference on Computer Vision, pp.346-361, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR'2016, pp.770-778, 2016.

G. E. Hinton and R. R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, issue.5786, pp.504-507, 2006.

V. Holub and J. Fridrich, Low-complexity features for jpeg steganalysis using undecimated dct, Information Forensics and Security, IEEE Trans. on, vol.10, issue.2, pp.219-228, 2015.

A. D. Ker and T. Pevný, Batch steganography in the real world, Proc. of the on Multimedia and Security, vol.12, pp.1-10, 2012.

A. D. Ker and T. Pevný, The steganographer is the outlier : realistic large-scale steganalysis, Information Forensics and Security, IEEE Trans. on, vol.9, issue.9, pp.1424-1435, 2014.

J. Kodovsk?, J. Fridrich, and V. Holub, Ensemble classifiers for steganalysis of digital media, Information Forensics and Security, IEEE Trans. on, vol.7, issue.2, pp.432-444, 2012.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Proceeding of Advances in Neural Information Processing Systems 25, NIPS'2012, pp.1097-1105, 2012.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, vol.521, pp.436-444, 2015.

E. Lehmann and J. Romano, Testing Statistical Hypotheses, 2005.

T. Pevný, P. Bas, and J. Fridrich, Steganalysis by subtractive pixel adjacency matrix, IEEE Trans. Inform. Forensics and Security, vol.5, issue.2, pp.215-224, 2010.

T. Pevný, J. Fridrich, and A. D. Ker, From blind to quantitative steganalysis, Proc. IS&T Electronic Imaging, 2009.

T. Pevný and A. D. Ker, Tow²ysis, in Proc. SPIE, vol.9409, pp.94090-94090, 2015.

T. Pevny and I. Nikolaev, Optimizing pooling function for pooled steganalysis, Proc. IEEE Intl' Workshop on Information Forensics and Security, pp.1-6, 2015.

Y. Qian, J. Dong, W. Wang, and T. Tan, Deep Learning for Steganalysis via Convolutional Neural Networks, Proc. IS&T Electronic Imaging, vol.9409, 2015.

V. Sedighi, R. Cogranne, and J. Fridrich, Content-adaptive steganography by minimizing statistical detectability, IEEE Trans. on Information Forensics and Security, vol.11, issue.2, pp.221-234, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01906608

V. Sedighi and J. Fridrich, Effect of Imprecise Knowledge of the Selection Channel on Steganalysis, Proc. ACM Workshop on Information Hiding and Multimedia Security, pp.33-42, 2015.

V. Sedighi, J. J. Fridrich, and R. Cogranne, Toss that bossbase, alice !, in Media Watermarking, Security, and Forensics, pp.1-9, 2016.

X. Song, F. Liu, C. Yang, X. Luo, and Y. Zhang, Steganalysis of adaptive jpeg steganography using 2d gabor filters, Proc. ACM Workshop on Information Hiding and Multimedia Security, pp.15-23, 2015.

T. H. Thai, R. Cogranne, and F. Retraint, Statistical model of quantized DCT coefficients : Application in the steganalysis of jsteg algorithm, Image Processing, IEEE Trans. on, vol.23, issue.0, pp.1-14, 2014.

A. Westfeld, F5-a steganographic algorithm, Proc. Information Hiding, vol.2137, pp.289-302, 2001.

A. Westfeld and A. Pfitzmann, Attacks on steganographic systems, Proc. Information Hiding, pp.61-76, 1999.

G. Xu, Deep Convolutional Neural Network to Detect J-UNIWARD, Proc. ACM Workshop on Information Hiding and Multimedia Security, pp.67-73, 2017.

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.

J. Ye, J. Ni, and Y. Yi, Deep Learning Hierarchical Representations for Image Steganalysis, IEEE Trans. on Information Forensics and Security, vol.12, issue.11, pp.2545-2557, 2017.

M. Yedroudj, M. Chaumont, and F. Comby, How to Augment a Small Learning Set for Improving the Performances of a CNN-Based Steganalyzer ?, Proc. IS&T Electronic Imaging, p.7, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01681883

M. Yedroudj, F. Comby, and M. Chaumont, Yedrouj-Net : An Efficient CNN for Spatial Steganalysis, Proc. of IEEE Intl' Conference on Acoustics, Speech and Signal Processing, pp.2092-2096, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01717550

Y. Yousfi, J. Butora, J. Fridrich, and Q. Giboulot, Breaking alaska : Color separation for steganalysis in jpeg domain, Proc. ACM Workshop on Information Hiding and Multimedia Security, pp.138-149, 2019.

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, p.6, 2017.

J. Zeng, S. Tan, B. Li, and J. Huang, Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework, IEEE Trans. on Information Forensics and Security, vol.13, issue.5, pp.1200-1214, 2018.

R. Zhang, F. Zhu, J. Liu, and G. Liu, Depth-wise separable convolutions and multi-level pooling for an efficient spatial cnn-based steganalysis, IEEE Trans. on Information Forensics and Security, vol.15, pp.1138-1150, 2020.