Optimal Transport as a Defense Against Adversarial Attacks
This work aims to improve the robustness of deep learning models against adversarial attacks, which is a critical problem for the security and reliability of AI systems, particularly in sensitive applications. This is an incremental improvement.
This paper addresses the vulnerability of deep learning classifiers to adversarial attacks by proposing Sinkhorn Adversarial Training (SAT), a defense mechanism that uses optimal transport theory to align original and adversarial image representations. The authors also introduce the Area Under the Accuracy Curve (AUAC) metric to quantify robustness across varying perturbation sizes. Experiments on CIFAR-10 and CIFAR-100 datasets demonstrate that SAT is more robust than state-of-the-art defenses.
Deep learning classifiers are now known to have flaws in the representations of their class. Adversarial attacks can find a human-imperceptible perturbation for a given image that will mislead a trained model. The most effective methods to defend against such attacks trains on generated adversarial examples to learn their distribution. Previous work aimed to align original and adversarial image representations in the same way as domain adaptation to improve robustness. Yet, they partially align the representations using approaches that do not reflect the geometry of space and distribution. In addition, it is difficult to accurately compare robustness between defended models. Until now, they have been evaluated using a fixed perturbation size. However, defended models may react differently to variations of this perturbation size. In this paper, the analogy of domain adaptation is taken a step further by exploiting optimal transport theory. We propose to use a loss between distributions that faithfully reflect the ground distance. This leads to SAT (Sinkhorn Adversarial Training), a more robust defense against adversarial attacks. Then, we propose to quantify more precisely the robustness of a model to adversarial attacks over a wide range of perturbation sizes using a different metric, the Area Under the Accuracy Curve (AUAC). We perform extensive experiments on both CIFAR-10 and CIFAR-100 datasets and show that our defense is globally more robust than the state-of-the-art.