Beyond Deepfake vs Real: Facial Deepfake Detection in the Open-Set Paradigm

Facial forgery methods such as deepfakes can be misused for identity manipulation and spreading misinformation. They have evolved alongside advancements in generative AI, leading to new and more sophisticated forgery techniques that diverge from existing ``known" methods. Conventional deepfake detection methods use the closed-set paradigm, thus limiting their applicability to detecting forgeries created using methods that are not part of the training dataset. In this paper, we propose a shift from the closed-set paradigm for deepfake detection. In the open-set paradigm, models are designed not only to identify images created by known facial forgery methods but also to identify and flag those produced by previously unknown methods as `unknown' and not as unforged or real or nmanipulated. In this paper, we propose an open-set deepfake classification algorithm based on supervised contrastive learning. The open-set paradigm used in our model allows it to function as a more robust tool capable of handling emerging and unseen deepfake techniques, enhancing reliability and confidence, and complementing forensic analysis. In the open-set paradigm, we identify three groups, including the `unknown' group that is neither considered a known deepfake nor real. We investigate deepfake open-set classification across three scenarios: classifying deepfakes from unknown methods not as real, distinguishing real images from deepfakes, and classifying deepfakes from known methods, using the FaceForensics++ dataset as a benchmark. Our method achieves state-of-the-art results in the first two tasks and competitive results in the third task.