An Agentic ENF Framework for Forensic Video Authentication Against Generative AI
Generative models now synthesise video that is hard to distinguish from genuine footage, and learned detectors that key on a generator's artefacts degrade sharply on unseen models. This paper develops a forensic framework that authenticates video by a signal that generation does not control: the electric network frequency (ENF), the mains-driven hum in audio and flicker in video. A unified two-channel detector extracts the ENF from audio and from rolling-shutter video through one second harmonic estimator and a shared network; a synthetic-injection training method, driven by an Ornstein-Uhlenbeck drift model and anchored to real recordings, overcomes the scarcity of labelled ENF data; a r...
| Authors | Ryan Wyton, Bacha Rehman |
|---|---|
| Primary category | cs.CR |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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