FIDA: Feature Instability-Driven Attack on Self-Supervised Facial Representation

FIDA (Feature Instability-Driven Attack) is a new backdoor attack framework targeting self-supervised facial representation models. It employs subtle semantic triggers and a novel Feature Instability Loss to make the encoder more sensitive to perturbations, thereby avoiding the rigid feature patterns seen in earlier attacks. Experiments demonstrate that FIDA achieves high attack success while largely preserving normal model performance.

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