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Hidden Poison: Machine unlearning enables camouflaged poisoning attacks
Jimmy Di · Jack Douglas · Jayadev Acharya · Gautam Kamath · Ayush Sekhari

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in Workshop on Machine Learning Safety »

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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Author Information

Jimmy Di (University of Waterloo)
Jack Douglas (University of Waterloo)
Jayadev Acharya (Cornell University)
Gautam Kamath (University of Waterloo)
Ayush Sekhari (Cornell University)

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