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This paper proposes a training-time backdoor defense called Trapping and Removing (TR), which introduces a lightweight shortcut branch as a honeypot to trap backdoor knowledge and then discards it, enhanced by a knowledge decoupling strategy with entropy-based weight assignment.
HARVEY learns a backdoored reference model to accurately identify poisonous samples, achieving near-perfect backdoor removal with minimal accuracy loss.