Integration of Multimodal Coherence Features for Robust and Explainable Deepfake Detection with Clinical Dentistry Use Cases - Karol JĘDRASIAK; Julia BIJOCH (Communications of International Proceedings, 2025)
This study presents a reproducible multimodal deepfake detection framework integrating visual, acoustic, and cross-modal coherence features for dental applications. Using the DeepFake RealWorld dataset (46,371 clips; 77% with audio), forty-seven interpretable descriptors were extracted across visual and bioacoustic domains. Cross-modal metrics, such as LSE-D/LSE-C and Δt₍AV₎, achieved the highest accuracy (Δp=0.21), face–voice coherence (Δp=0.19), and scene–audio consistency (Δp=0.18). Acoustic markers such as RT₆₀ and DRR reached Δp=0.16 with
Keywords: Deepfake detection; multimodal coherence; audio–video synchronization; XAI; forensic analysis; teledentistry