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Clean-label dormant backdoors can remain hidden until specific data is unlearned, enabling stealthy and effective exploitation without immediate detection.
MPLCE achieves a remarkable 98.61% genuine acceptance rate while eliminating the risks of helper data and maintaining classification consistency across different biometric modalities.
Querying the right samples can reduce black-box MIA costs by over 80% without sacrificing accuracy.