This is the code release for our paper "Evasion Attack STeganography: Turning Vulnerability Of Machine Learning To Adversarial Attacks Into A Real-world Application"
Published in ICCV2021 - AROW
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. The key idea of EAST is to encode data as the labels of the image that the evasion attacks produce. 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 out-performs existing deep-learning-based steganography approaches with images that are 70% denser and 73% more robust and supports multiple datasets and architectures.
Recommended citation: Ghamizi, S., Cordy, M., Papadakis, M., & Traon, Y.L. (2021). Evasion Attack STeganography: Turning Vulnerability Of Machine Learning To Adversarial Attacks Into A Real-world Application. Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision.
This project is now a Pytorch project. If you want the previous version (with SATA algorithm and Tensorflow), please check the branch "master-SATA"
The "main" branch will contain the new code and updates of our Steganography package (With Pytorch).
The code should be run using python 3.8, torch==1.7.1. We recommend using conda for a dedicated environment
You can use either conda or pip to install the project requirements.
sudo pip install -r ./requirements.txt