With the continuous improvements of deepfake methods, forgery messages have
transitioned from single-modality to multi-modal fusion, posing new challenges
for existing forgery detection algorithms. In this paper, we propose AVT2-DWF,
the Audio-Visual dual Transformers grounded in Dynamic Weight Fusion, which
aims to amplify both intra- and cross-modal forgery cues, thereby enhancing
detection capabilities. AVT2-DWF adopts a dual-stage approach to capture both
spatial characteristics and temporal dynamics of facial expressions. This is
achieved through a face transformer with an n-frame-wise tokenization strategy
encoder and an audio transformer encoder. Subsequently, it uses multi-modal
conversion with dynamic weight fusion to address the challenge of heterogeneous
information fusion between audio and visual modalities. Experiments on
DeepfakeTIMIT, FakeAVCeleb, and DFDC datasets indicate that AVT2-DWF achieves
state-of-the-art performance intra- and cross-dataset Deepfake detection. Code
is available at this https URL

本文提出了 AVT2-DWF，通过音视频双重变换和动态权重融合，增强了单一伪造模态和多模态融合下的伪造检测能力，在 DeepfakeTIMIT，FakeAVCeleb 和 DFDC 数据集上取得了最先进的性能。