Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks

Understand

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.