Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack 事件

Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack arXiv:2506.01318v4 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques overlook two critical blind spots: "over-unlearning" that deteriorates retained data near the forget set, and post-hoc "relearning" attacks that aim to resurrect the forgotten knowledge. Focusing on class-level unlearning, w