UNIMELB_BAHAVAN-Nadarasar_VYT-LOCAL-2025.mp4

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 research, 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 un-forged/real/unmanipulated.