Why adversarial training can hurt robust accuracy

Abstract

Machine learning classifiers with high test accuracy often perform poorly under adversarial attacks. It is commonly believed that adversarial training alleviates this issue. In this paper, we demonstrate that, surprisingly, the opposite may be true -Even though adversarial training helps when enough data is available, it may hurt robust generalization in the small sample size regime. We first prove this phenomenon for a high-dimensional linear classification setting with noiseless observations. Our proof provides explanatory insights that may also transfer to feature learning models. Further, we observe in experiments on standard image datasets that the same behavior occurs for perceptible attacks that effectively reduce class information such as mask attacks and object corruptions.

Pelta

“…However, recent studies show a trade-off between a model's generalization capabilities (i.e., its standard test accuracy) and its robust accuracy [14,34,39,47]. AT can also expose the model to new threats [53] and, perhaps even more remarkably, increase robust error at times [15]. It is possible to use AT at training time as a defense in the federated learning context [13] but it creates its own sensitive exposure to a potentially malicious server.…”