Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning

Autor:	Yang, Yulong, Lin, Chenhao, Ji, Xiang, Tian, Qiwei, Li, Qian, Yang, Hongshan, Wang, Zhibo, Shen, Chao
Rok vydání:	2023
Předmět:	Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Transfer-based adversarial attacks raise a severe threat to real-world deep learning systems since they do not require access to target models. Adversarial training (AT), which is recognized as the strongest defense against white-box attacks, has also guaranteed high robustness to (black-box) transfer-based attacks. However, AT suffers from heavy computational overhead since it optimizes the adversarial examples during the whole training process. In this paper, we demonstrate that such heavy optimization is not necessary for AT against transfer-based attacks. Instead, a one-shot adversarial augmentation prior to training is sufficient, and we name this new defense paradigm Data-centric Robust Learning (DRL). Our experimental results show that DRL outperforms widely-used AT techniques (e.g., PGD-AT, TRADES, EAT, and FAT) in terms of black-box robustness and even surpasses the top-1 defense on RobustBench when combined with diverse data augmentations and loss regularizations. We also identify other benefits of DRL, for instance, the model generalization capability and robust fairness. Comment: 9 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.09891 Zobrazit plný text záznamu View this record from Arxiv