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
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
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
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
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
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
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
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
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
Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015. ,
DOI : 10.1007/s10994-013-5335-x
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
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
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
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
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
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
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. ,
Network in network, International Conference on Learning Representations, ICLR 2014, p.10, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01551350
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
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
???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
Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.675-678, 2014. ,
DOI : 10.1145/2647868.2654889
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. ,
BOWS-2 Contest (Break Our Watermarking System) Organized between the 17th of, 2007. ,
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