Rémi Cogranne

University of Technology of Troyes (UTT), France
close up of a man with dark hair
© Remi Cogranne
Statistical Models of digital images for Adversarial Methods in steganography and AI-based generation

Rémi Cogranne 01/01

Rémi Cogranne received the Ph.D. degree in systems safety and optimization from the University of Technology of Troyes (UTT), France, in 2011. He was regularly invited as a Visiting Scholar with Binghamton University, from 2014 to 2020. He has been an Associate Professor with UTT, since 2013. His research interests include hypothesis testing applied to image forensics, steganalysis, steganography, and computer networks. He has published more than 80 articles, among which two received the best paper awards, and three International patents. He is an Elected Member of IEEE Technical Committee on IFS a member of the Editorial Board of several international journals and conferences. He has been the general chair of IEEE WIFS 2022, the General Chair of ACM IH&MMSec 2019, the TPC Chair of IEEE WIFS 2021, and the Main Organizer of ALASKA Steganalysis Challenge (https://alaska.utt.fr).

Statistical Models of digital images for Adversarial Methods in steganography and AI-based generation

While a vast majority of digital image forensics approaches are based on machine learning and, recently, exploits the extremely high accuracy of deep learning, these approaches generally provide a low-level of understanding and interpretability.
In the speech, we will present statistical models that allow assessing detectability of information hidden in digital images. We will review how such models can be used to design original adversarial methods that minimizes the statistical detectability.
Last, but not least, we will study the possible application of a similar adversarial method for AI-based generation of digital images.

CUING

The 8th International Workshop on Cyber Use of Information Hiding
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