Building Robust Video-Level Deepfake Detection via Audio-Visual Local-Global Interactions

The continual advancements in Generative Artificial Intelligence have created substantial hurdles for accurate deepfake detection, leading to limitations of currently popular detection methods across content-driven video-level deepfake detection scenarios. In this paper, we present the solutions to the Video-Level Deepfake Detection task. Our empirical findings demonstrate that modeling correlations of audio-visual modalities is important for video-level deepfake detection. Therefore, we introduce the model denoted Audio-Visual Local-Global Neural Network (i.e., AV-LGNN) in which the core design is the proposed AV-LGI Module (Audio-Visual Local-Global Interaction Module). The AV-LGI Module is composed of three stages: Local Intra-Region Interaction, Global Inter-Region Interaction, and Local-Global Interaction, which can better capture detailed information at local-level and efficiently learn the fine-grained correlations of inter-modalities in video deepfake detection under lower computational overheads. We further propose an adaptive modality selection strategy to facilitate model learning. Besides, a variety of data augmentation techniques are incorporated for audio-visual branches to enhance the robustness of the AV-LGNN. The experimental results verify the effectiveness of our model.