PulseGuard: rPPG-Derived Temporal Signals as a Forensic Modality for Talking-Face Deepfake Detection
Othmane Harraq, Tamer Aldwairi
原文摘要
Talking-face (TF) deepfake generation synthesizes photorealistic facial video from a static source image and an audio signal, producing forgeries that current image-based detectors consistently fail to identify. Unlike face-swap manipulation, TF synthesis has no underlying real video from which to inherit physiological characteristics, motivating remote photoplethysmography (rPPG) as a detection modality for this forgery category. We propose PulseGuard, a detection framework that extracts per-window rPPG waveforms via RhythmFormer and trains a suite of lightweight classifiers to distinguish real from synthesized traces. Evaluated on the TF subset of Celeb-DF++ under a strict subject-independent protocol, where test identities are completely separated from training identities, our 1D ResNet achieves a window-level AUC of 0.826 at EER 25.1% (0.842 pooled to video level, the granularity at which DeepFakesON-Phys is scored) while operating exclusively on the extracted temporal signal, and retains AUC 0.754 on generators withheld entirely from training. We document a controlled reproduction study of DeepFakesON-Phys, the representative prior rPPG detector, demonstrating degradation from AUC 0.999 on legacy face-swap data to 0.576 on the TF subset of Celeb-DF++. We further show that detection difficulty is strongly method-dependent: AUC ranges from 0.937 (Real3DPortrait) to 0.617 (IP-LAP) across the seven TF generators, with the ordering broadly preserved under per-method training. This spread is stable across training regimes rather than reflecting evaluation noise, and constitutes the primary empirical contribution of the work. We report these as empirical baselines and do not establish that the discriminative content is cardiac in origin.
cs.LG · cs.CR · cs.CV · cs.MM