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

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

Abstract:Self-blended images are widely used to train face-swap detectors, but primarily capture blending artifacts. We investigate whether adding illumination inconsistencies improves detection. Temporal Self-Blended Images (T-SBI) transfer lighting statistics between frames of the same video, with the mismatch controlled by luminance difference ({\Delta}L). Using five training regimes and a three-seed comparison of high- and low-{\Delta}L training, we find no evidence of illumination-specific improvements. AUC differences remain within seed variability across four datasets, and an analysis of 506,328 attribute-binned samples shows no preferential reduction in errors under harsh lighting. Instead, T-SBI shifts prediction scores, changing optimal thresholds by approximately 0.34 on FaceForensics++ and 0.30 on Celeb-DF, making comparisons at a fixed threshold misleading. 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. These findings highlight the importance of evaluating training methods against their intended targets and accounting for threshold effects.

Machine Learning (cs.LG)

arXiv:2610.11706 [cs.LG]

(or arXiv:2610.11706v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.11706 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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