Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor Attack

• URL: http://arxiv.org/abs/2301.02615v2
• Date: Sun, 1 Oct 2023 16:32:23 GMT
• Title: Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor Attack
• Authors: Tzvi Lederer, Gallil Maimon and Lior Rokach
• Abstract summary: We introduce Silent Killer, a novel attack that operates in clean-label, black-box settings. We investigate the use of universal adversarial perturbations as triggers in clean-label attacks. We find that gradient alignment for crafting the poison is required to ensure high success rates.
• Score: 10.047470656294335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor poisoning attacks pose a well-known risk to neural networks. However, most studies have focused on lenient threat models. We introduce Silent Killer, a novel attack that operates in clean-label, black-box settings, uses a stealthy poison and trigger and outperforms existing methods. We investigate the use of universal adversarial perturbations as triggers in clean-label attacks, following the success of such approaches under poison-label settings. We analyze the success of a naive adaptation and find that gradient alignment for crafting the poison is required to ensure high success rates. We conduct thorough experiments on MNIST, CIFAR10, and a reduced version of ImageNet and achieve state-of-the-art results.

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