LATIDIA · Ciberseguridad

Un marco ENF genético para la autenticación de vídeo forense contra la IA generativa

arXiv: 2610.22440v1Anunciar Tipo: nuevo Resumen: Los modelos generativos ahora sintetizan video que es difícil de distinguir de las imágenes genuinas, y los detectores aprendidos que son clave en los artefactos de un generador se degradan bruscamente cuando no se ven

La noticia

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 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 reference-matching channel