TriDF: Evaluating Perception, Detection, and Hallucination for Interpretable DeepFake Detection

ArXi:2512.10652v3 Announce Type: replace Advances in generative modeling have made it increasingly easy to fabricate realistic portrayals of individuals, creating serious risks for security, communication, and public trust. Detecting such person-driven manipulations requires systems that not only distinguish altered content from authentic media but also provide clear and reliable reasoning. In this paper, we