Feature Squeezing is a recently proposed defense method which reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. It has been shown that feature squeezing defenses can be combined in a joint detection framework to achieve high detection rates against state-of-the-art attacks. However, we demonstrate on the MNIST and CIFAR-10 datasets that by increasing the adversary strength of said state-of-the-art attacks, one can bypass the detection framework with adversarial examples of minimal visual distortion. These results suggest for proposed defenses to validate against stronger attack configurations.
Cite
@article{arxiv.1803.09868,
title = {Bypassing Feature Squeezing by Increasing Adversary Strength},
author = {Yash Sharma and Pin-Yu Chen},
journal= {arXiv preprint arXiv:1803.09868},
year = {2018}
}