  This paper addresses the challenge of developing a robust audio-visual
deepfake detection model. In practical use cases, new generation algorithms are
continually emerging, and these algorithms are not encountered during the
development of detection methods. This calls for the generalization ability of
the method. Additionally, to ensure the credibility of detection methods, it is
beneficial for the model to interpret which cues from the video indicate it is
fake. Motivated by these considerations, we then propose a multi-stream fusion
approach with one-class learning as a representation-level regularization
technique. We study the generalization problem of audio-visual deepfake
detection by creating a new benchmark by extending and re-splitting the
existing FakeAVCeleb dataset. The benchmark contains four categories of fake
video(Real Audio-Fake Visual, Fake Audio-Fake Visual, Fake Audio-Real Visual,
and unsynchronized video). The experimental results show that our approach
improves the model's detection of unseen attacks by an average of 7.31% across
four test sets, compared to the baseline model. Additionally, our proposed
framework offers interpretability, indicating which modality the model
identifies as fake.
