Boosting Active Defense Persistence: A Two-Stage Defense Framework Combining Interruption and Poisoning Against Deepfake

This research addresses a weakness in active defense systems against deepfakes (AI-generated fake videos or images): these defenses often fail when attackers retrain their models on protected samples. The authors propose a Two-Stage Defense Framework (TSDF) that uses dual-function adversarial perturbations (carefully designed noise patterns that disrupt both the deepfake output and the attacker's retraining process) to make defenses more persistent by poisoning the data (corrupting the training information) that attackers would use to adapt their models.