How to choose your best allies for a transferable attack?

• URL: http://arxiv.org/abs/2304.02312v2
• Date: Sun, 16 Jul 2023 14:36:20 GMT
• Title: How to choose your best allies for a transferable attack?
• Authors: Thibault Maho, Seyed-Mohsen Moosavi-Dezfooli, Teddy Furon
• Abstract summary: Transferability of adversarial examples is a key issue in the security of deep neural networks. New tool shows that transferable attacks may perform far worse than a black box attack if the attacker randomly picks the source model. FiT is highly effective at selecting the best source model for multiple scenarios.
• Score: 26.669765474142995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transferability of adversarial examples is a key issue in the security of deep neural networks. The possibility of an adversarial example crafted for a source model fooling another targeted model makes the threat of adversarial attacks more realistic. Measuring transferability is a crucial problem, but the Attack Success Rate alone does not provide a sound evaluation. This paper proposes a new methodology for evaluating transferability by putting distortion in a central position. This new tool shows that transferable attacks may perform far worse than a black box attack if the attacker randomly picks the source model. To address this issue, we propose a new selection mechanism, called FiT, which aims at choosing the best source model with only a few preliminary queries to the target. Our experimental results show that FiT is highly effective at selecting the best source model for multiple scenarios such as single-model attacks, ensemble-model attacks and multiple attacks (Code available at: https://github.com/t-maho/transferability_measure_fit).

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