Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning AttacksDownload PDF

Published: 21 Nov 2022, Last Modified: 14 Apr 2024TSRML2022Readers: Everyone
Keywords: Machine Unlearning, Poisoning Attack, Camouflaging Poisons
TL;DR: We show that machine unlearning can be used to implement a new type of camouflaged data poisoning attack.
Abstract: We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset.
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