AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
<|reference_start|>AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies: 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 https://github.com/raining-dev/AVT2-DWF.<|reference_end|>
@article{wang2024avt2-dwf:,
    title={AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and
  Dynamic Weighting Strategies},
    author={Rui Wang, Dengpan Ye, Long Tang, Yunming Zhang, Jiacheng Deng},
    journal={arXiv preprint arXiv:2403.14974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14974},
    primaryClass={cs.CV}
}