Interpretable facial dynamics as behavioral and perceptual traces of deepfakes
Deepfake detection research has largely converged on deep learning approaches that, despite strong benchmark performance, offer limited insight into what distinguishes real from manipulated facial behavior. This study presents an interpretable alternative grounded in bio-behavioral features of facial dynamics and evaluates how computational detection strategies relate to human perceptual judgments. We identify core low-dimensional patterns of facial movement, from which temporal features characterizing spatiotemporal structure were derived. Traditional machine learning classifiers trained on these features achieved modest but significant above-chance deepfake classification, driven by hig...
| Authors | Timothy Joseph Murphy, Jennifer Cook, Hélio Clemente José Cuve |
|---|---|
| Primary category | cs.CV |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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