Preemptive Defense Algorithm Based on Generalizable Black-Box Feedback Regulation Strategy Against Face-Swapping Deepfake Models

In the previous efforts to counteract Deepfake, detection methods were most adopted, but they could only function after-effect and could not undo the harm. Preemptive defense has recently gained attention as an alternative, but such defense works have either limited their scenario to facial-reenactment Deepfake models or only targeted specific face-swapping Deepfake model. Motivated to fill this gap, we start by establishing the Deepfake scenario modeling and finding the scenario difference among categories, then move on to the face-swapping scenario setting overlooked by previous works. Based on this scenario, we first propose a novel Black-Box Penetrating Defense Process that enables defense against face-swapping models without prior model knowledge. Then we propose a novel Double-Blind Feedback Regulation Strategy to solve the reality problem of avoiding alarming distortions after defense that had previously been ignored, which helps conduct valid preemptive defense against face-swapping Deepfake models in reality. Experimental results in comparison with state-of-the-art defense methods are conducted against popular face-swapping Deepfake models, proving our proposed method valid under practical circumstances.