Attribution-Guided Multimodal Deepfake Detection via Cross-Modal Forensic Fingerprints

ArXi:2604.26453v1 Announce Type: new Audio-visual deepfakes have reached a level of realism that makes perceptual detection unreliable, threatening media integrity and biometric security. While multimodal detection has shown promise, most approaches are binary classification tasks that often latch onto dataset-specific artifacts rather than genuine generative traces. We argue that a detector incapable of identifying how a video was forged is likely learning the wrong signal.