  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. Then, the edit distance between the two sequences is
computed to assess whether the claimed video is genuine. Experimental results
indicate that, compared to two mainstream approaches based on semantic
consistency and temporal consistency, our approach achieves superior
generalizability across various deepfake techniques and demonstrates strong
robustness against audio-visual perturbations. Finally, state-of-the-art
performance gains can be achieved by simply integrating the decision scores of
these three systems.
