An Agentic ENF Framework for Forensic Video Authentication Against Generative AI
An agentic framework using electrical network frequency traces authenticates video independently of the generative model, offering a physics-grounded alternative to brittle learned detectors.
Summary written by editorial AI · Source link below
arXiv:2610.22440v1 Announce Type: new Abstract: 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
Editorial Analysis
As deepfake detectors trained on known generators fail on unseen models, physics-based authentication offers enterprises a more durable forensic anchor.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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