Analysis of Lighting and Shape Features in Deepfake Identification

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.