@article{jedrasiak2025empirical,
  title = {Empirical Analysis of Textural and Edge Features for Deepfake Detection},
  author = {Karol JEDRASIAK},
  year = 2025,
  url = {https://ibimapublishing.com/p-articles/46AI/2025/4632525/},
  journal = {Communications of International Proceedings},
  volume = 2025 (34),
  doi = doi.org/10.5171/2025.4632525,
  abstract = {This study evaluates interpretable textural and edge descriptors for deepfake detection under real-world conditions. Using the new DFRW dataset with 46 371 clips from diffusion and face-swap models, classical features showed strong robustness to compression and re-encoding. The best-performing descriptors: CLBP, LBP, BSIF, HOG, structure tensor coherence, MBH, and checkerboard index, achieved Δp≈0.25–0.30 with stable accuracy above 720p. The results provide quantitative evidence that physics-based, explainable features can reliably separate fake from real content, advancing transparent forensic detection.},
  keywords = {Deepfake detection, interpretable features, texture and edge analysis, forensic analysis},
  note = Article ID: 4632525
}