Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery
A robust dynamic-expansion framework that integrates sample purification, selective recovery, and robust expert routing into a unified continual learning paradigm is proposed, and Bi-Prototype Purification (BPP) is introduced to identify suspicious samples by exploiting semantic discrepancies in feature space.