Adaptive Affinity Memorization With Layer Mutation for Multimodal Deepfake Continual Detection

Summary

Amber (Adaptive Affinity Memorization with Layer Mutation) is a continual learning method for Multimodal Deepfake Continual Detection (MDCD), the task of detecting new multimodal deepfake techniques as they emerge. The authors argue that vanilla continual learning fails here because artifact feature drift, caused by the cross-modal gap, makes preserved memory unrepresentative and drives forgetting of artifact features. Amber combines Memorization Affinity Estimation, which keeps high- and low-affinity memory, with Knowledge Layer-wise Mutation, which broadens historical memory, and is evaluated with a new metric, initial comprehension capability (ICC). The authors report that extensive experiments show Amber adapts to new deepfake techniques while retaining prior knowledge.

Topics

• Deepfakes and impersonation