Zero-Shot Fake Video Detection by Audio-Visual Consistency

Understand

Recent studies have advocated the detection of fake videos as a one-class detection task, predicated on the hypothesis that the consistency between audio and visual modalities of genuine data is more significant than that of fake data.

• This methodology, which solely relies on genuine audio-visual data while negating the need for forged counterparts, is thus delineated as a `zero-shot' detection paradigm.
• This paper introduces a novel zero-shot detection approach anchored in content consistency across audio and video.
• By employing pre-trained ASR and VSR models, we recognize the audio and video content sequences, respectively.