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Backdoor poisoning attacks pose a well-known risk to neural networks.However, most studies have focused on lenient threat models. We introduceSilent Killer, a novel attack that operates in clean-label, black-box settings,uses a stealthy poison and trigger and outperforms existing methods. Weinvestigate the use of universal adversarial perturbations as triggers inclean-label attacks, following the success of such approaches underpoison-label settings. We analyze the success of a naive adaptation and findthat gradient alignment for crafting the poison is required to ensure highsuccess rates. We conduct thorough experiments on MNIST, CIFAR10, and a reducedversion of ImageNet and achieve state-of-the-art results.

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