Reference : Evasion Attack STeganography: Turning Vulnerability Of Machine Learning ToAdversarial...
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/47832
Evasion Attack STeganography: Turning Vulnerability Of Machine Learning ToAdversarial Attacks Into A Real-world Application
English
Ghamizi, Salah mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Cordy, Maxime mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
Papadakis, Mike mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Le Traon, Yves mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
11-Oct-2021
Proceedings of International Conference on Computer Vision 2021
Yes
International
[en] Adversarial Attacks ; Steganography ; Watermarking
[en] Evasion Attacks have been commonly seen as a weakness of Deep Neural Networks. In this paper, we flip the paradigm and envision this vulnerability as a useful application.

We propose EAST, a new steganography and watermarking technique based on multi-label targeted evasion attacks.

Our results confirm that our embedding is elusive; it not only passes unnoticed by humans, steganalysis methods, and machine-learning detectors. In addition, our embedding is resilient to soft and aggressive image tampering (87% recovery rate under jpeg compression). EAST outperforms existing deep-learning-based steganography approaches with images that are 70% denser and 73% more robust and supports multiple datasets and architectures.
Researchers
http://hdl.handle.net/10993/47832

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