We describe the Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is non-trivial as forgery-related knowledge is entangled with a wide range of unrelated knowledge. Existing methods treat CLIP merely as a feature extractor, lacking task-specific adaptation, which limits their effectiveness. To address this, we introduce an adapter to learn face forgery traces -- the blending boundaries unique to forged faces, guided by task-specific objectives. Then we enhance the CLIP visual tokens with a dedicated interaction strategy that communicates knowledge across CLIP and the adapter. Since the adapter is alongside CLIP, its versatility is highly retained, naturally ensuring strong generalizability in face forgery detection. With only $\bm{5.7M}$ trainable parameters, our method achieves a significant performance boost, improving by approximately $\bm{7\%}$ on average across five standard datasets. We believe the proposed method can serve as a baseline for future CLIP-based face forgery detection methods.

本研究提出“取证适配器”，旨在将CLIP转变为有效且通用的人脸伪造检测器。该方法通过学习伪造特征并针对特定任务进行适配，实现了对CLIP的充分利用，最终在多个标准数据集上平均提升了约7%的性能，具有重要的实用意义。

取证适配器：将CLIP适配用于通用人脸伪造检测