@article{jdrasiak2025integration,
  title = {Integration of Multimodal Coherence Features for Robust and Explainable Deepfake Detection with Clinical Dentistry Use Cases},
  author = {Karol JĘDRASIAK and Julia BIJOCH},
  year = 2025,
  url = {https://ibimapublishing.com/p-articles/46AI/2025/4638325/},
  journal = {Communications of International Proceedings},
  volume = 2025 (34),
  doi = doi.org/10.5171/2025.4638325,
  abstract = {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},
  note = Article ID: 4638325
}