PubDef: Defending Against Transfer Attacks From Public Models

• URL: http://arxiv.org/abs/2310.17645v2
• Date: Sun, 17 Mar 2024 04:40:48 GMT
• Title: PubDef: Defending Against Transfer Attacks From Public Models
• Authors: Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan, David Wagner,
• Abstract summary: We propose a new practical threat model where the adversary relies on transfer attacks through publicly available surrogate models. We evaluate the transfer attacks in this setting and propose a specialized defense method based on a game-theoretic perspective. Under this threat model, our defense, PubDef, outperforms the state-of-the-art white-box adversarial training by a large margin with almost no loss in the normal accuracy.
• Score: 6.0012551318569285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial attacks have been a looming and unaddressed threat in the industry. However, through a decade-long history of the robustness evaluation literature, we have learned that mounting a strong or optimal attack is challenging. It requires both machine learning and domain expertise. In other words, the white-box threat model, religiously assumed by a large majority of the past literature, is unrealistic. In this paper, we propose a new practical threat model where the adversary relies on transfer attacks through publicly available surrogate models. We argue that this setting will become the most prevalent for security-sensitive applications in the future. We evaluate the transfer attacks in this setting and propose a specialized defense method based on a game-theoretic perspective. The defenses are evaluated under 24 public models and 11 attack algorithms across three datasets (CIFAR-10, CIFAR-100, and ImageNet). Under this threat model, our defense, PubDef, outperforms the state-of-the-art white-box adversarial training by a large margin with almost no loss in the normal accuracy. For instance, on ImageNet, our defense achieves 62% accuracy under the strongest transfer attack vs only 36% of the best adversarially trained model. Its accuracy when not under attack is only 2% lower than that of an undefended model (78% vs 80%). We release our code at https://github.com/wagner-group/pubdef.

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