Component-Specific Prompt Tuning for Deepfake Detection

Deepfake technology can create fake facial images that are hard to distinguish from real ones, posing risks to privacy and security. This paper proposes a new detection method using Visual Language Models (VLMs, AI systems that understand both images and text) combined with component-specific prompt tuning (customizing input instructions to focus on specific facial parts like eyes and nose). The approach transforms deepfake detection into a Visual Question Answering task and uses a Q-Former module (a feature extraction component guided by instructions) to help the model identify forgery traces in local facial features, achieving better accuracy than existing methods.