Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Abstract

Unlearnable example attacks are data poisoning techniques that can be used to safeguard public data against unauthorized use for training deep learning models. These methods add stealthy perturbations to the original image, thereby making it difficult for deep learning models to learn from these training data effectively. Current research suggests that adversarial training can, to a certain degree, mitigate the impact of unlearnable example attacks, while common data augmentation methods are not effective against such poisons. Adversarial training, however, demands considerable computational resources and can result in non-trivial accuracy loss. In this paper, we introduce the UEraser method, which outperforms current defenses against different types of state-of-the-art unlearnable example attacks through a combination of effective data augmentation policies and loss-maximizing adversarial augmentations. In stark contrast to the current SOTA adversarial training methods, UEraser uses adversarial augmentations, which extends beyond the confines of $ \ell_p $ perturbation budget assumed by current unlearning attacks and defenses. It also helps to improve the model's generalization ability, thus protecting against accuracy loss. UEraser wipes out the unlearning effect with error-maximizing data augmentations, thus restoring trained model accuracies. Interestingly, UEraser-Lite, a fast variant without adversarial augmentations, is also highly effective in preserving clean accuracies. On challenging unlearnable CIFAR-10, CIFAR-100, SVHN, and ImageNet-subset datasets produced with various attacks, it achieves results that are comparable to those obtained during clean training. We also demonstrate its efficacy against possible adaptive attacks. Our code is open source and available to the deep learning community: https://github.com/lafeat/ueraser.