@article{jedrasiak2025analysis,
  title = {Analysis of Lighting and Shape Features in Deepfake Identification},
  author = {Karol JEDRASIAK},
  year = 2025,
  url = {https://ibimapublishing.com/p-articles/46AI/2025/4632625/},
  journal = {Communications of International Proceedings},
  volume = 2025 (34),
  doi = doi.org/10.5171/2025.4632625,
  abstract = {This study investigates interpretable photometric and geometric features for deepfake detection under realistic conditions. The dedicated DeepFake RealWorld (DFRW) dataset, comprising 46 371 clips generated by diffusion, reenactment, and face-swap models, was used to evaluate lighting and shape consistency. Key descriptors, including light direction mismatch (Δθ), luminance deviation (ΔL), shading ratio (r_shade), shadow coherence (χ_shadow), and head-torso alignment, achieved Δp≈0.20–0.23 and PR up to 4.25. The results confirm that physically grounded descriptors of illumination and geometry enable reliable, explainable deepfake detection in forensic contexts.},
  keywords = {Deepfake detection, photometric and geometric features, forensic video analysis},
  note = Article ID: 4632625
}