Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs against Query-Based Attacks

Autor:	Zimmer, Pascal, Andreina, Sébastien, Marson, Giorgia Azzurra, Karame, Ghassan
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Although promising, existing defenses against query-based attacks share a common limitation: they offer increased robustness against attacks at the price of a considerable accuracy drop on clean samples. In this work, we show how to efficiently establish, at test-time, a solid tradeoff between robustness and accuracy when mitigating query-based attacks. Given that these attacks necessarily explore low-confidence regions, our insight is that activating dedicated defenses, such as random noise defense and random image transformations, only for low-confidence inputs is sufficient to prevent them. Our approach is independent of training and supported by theory. We verify the effectiveness of our approach for various existing defenses by conducting extensive experiments on CIFAR-10, CIFAR-100, and ImageNet. Our results confirm that our proposal can indeed enhance these defenses by providing better tradeoffs between robustness and accuracy when compared to state-of-the-art approaches while being completely training-free. Comment: To appear in the Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.10132 Zobrazit plný text záznamu View this record from Arxiv