Does an Illumination Prior Help Face-Swap Detection? A Controlled Study of Temporal Self-Blended Images

Key points

• We investigate whether adding illumination inconsistencies improves detection.1
• Temporal Self-Blended Images (T-SBI) transfer lighting statistics between frames of the same video, with the mismatch controlled by luminance difference (ΔL).1
• Using five training regimes and a three-seed comparison of high- and low-ΔL training, we find no evidence of illumination-specific improvements.1
• However, T-SBI improves robustness to heavy JPEG compression on DFDC (AUC 0.780 versus 0.696), potentially reflecting greater reliance on low-frequency cues.1

Sources (1)

• [1]Does an Illumination Prior Help Face-Swap Detection? A Controlled Study of Temporal Self-Blended Images
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 11:12 AM

  Self-blended images are widely used to train face-swap detectors, but primarily capture blending artifacts.

  We investigate whether adding illumination inconsistencies improves detection.

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