  Forged content shared widely on social media platforms is a major social
problem that requires increased regulation and poses new challenges to the
research community. The recent proliferation of hyper-realistic deepfake videos
has drawn attention to the threat of audio and visual forgeries. Most previous
work on detecting AI-generated fake videos only utilizes visual modality or
audio modality. While there are some methods in the literature that exploit
audio and visual modalities to detect forged videos, they have not been
comprehensively evaluated on multi-modal datasets of deepfake videos involving
acoustic and visual manipulations. Moreover, these existing methods are mostly
based on CNN and suffer from low detection accuracy. Inspired by the recent
success of Transformer in various fields, to address the challenges posed by
deepfake technology, in this paper, we propose an Audio-Visual
Transformer-based Ensemble Network (AVTENet) framework that considers both
acoustic manipulation and visual manipulation to achieve effective video
forgery detection. Specifically, the proposed model integrates several purely
transformer-based variants that capture video, audio, and audio-visual salient
cues to reach a consensus in prediction. For evaluation, we use the recently
released benchmark multi-modal audio-video FakeAVCeleb dataset. For a detailed
analysis, we evaluate AVTENet, its variants, and several existing methods on
multiple test sets of the FakeAVCeleb dataset. Experimental results show that
our best model outperforms all existing methods and achieves state-of-the-art
performance on Testset-I and Testset-II of the FakeAVCeleb dataset.
