Catastrophic overfitting can be induced with discriminative non-robust features
This work addresses a critical failure mode in adversarial training for machine learning practitioners, offering incremental understanding of its causes.
The paper tackled the problem of catastrophic overfitting in adversarial training by showing that injecting images with discriminative non-robust features can induce this failure at smaller perturbation levels than previously observed, leading to a breakdown in robustness. The result provides new insights into the mechanisms behind this phenomenon through controlled experiments.
Adversarial training (AT) is the de facto method for building robust neural networks, but it can be computationally expensive. To mitigate this, fast single-step attacks can be used, but this may lead to catastrophic overfitting (CO). This phenomenon appears when networks gain non-trivial robustness during the first stages of AT, but then reach a breaking point where they become vulnerable in just a few iterations. The mechanisms that lead to this failure mode are still poorly understood. In this work, we study the onset of CO in single-step AT methods through controlled modifications of typical datasets of natural images. In particular, we show that CO can be induced at much smaller $ε$ values than it was observed before just by injecting images with seemingly innocuous features. These features aid non-robust classification but are not enough to achieve robustness on their own. Through extensive experiments we analyze this novel phenomenon and discover that the presence of these easy features induces a learning shortcut that leads to CO. Our findings provide new insights into the mechanisms of CO and improve our understanding of the dynamics of AT. The code to reproduce our experiments can be found at https://github.com/gortizji/co_features.