ERF-BA-TFD+: A Multimodal Model for Audio-Visual Deepfake Detection

• URL: http://arxiv.org/abs/2508.17282v1
• Date: Sun, 24 Aug 2025 10:03:46 GMT
• Title: ERF-BA-TFD+: A Multimodal Model for Audio-Visual Deepfake Detection
• Authors: Xin Zhang, Jiaming Chu, Jian Zhao, Yuchu Jiang, Xu Yang, Lei Jin, Chi Zhang, Xuelong Li,
• Abstract summary: We present ERF-BA-TFD+, a novel deepfake detection model that combines enhanced receptive field (ERF) and audio-visual fusion.<n>Our model processes both audio and video features simultaneously, leveraging their complementary information to improve detection accuracy and robustness.<n>We evaluate ERF-BA-TFD+ on the DDL-AV dataset, which consists of both segmented and full-length video clips.
• Score: 49.14187862877009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfake detection is a critical task in identifying manipulated multimedia content. In real-world scenarios, deepfake content can manifest across multiple modalities, including audio and video. To address this challenge, we present ERF-BA-TFD+, a novel multimodal deepfake detection model that combines enhanced receptive field (ERF) and audio-visual fusion. Our model processes both audio and video features simultaneously, leveraging their complementary information to improve detection accuracy and robustness. The key innovation of ERF-BA-TFD+ lies in its ability to model long-range dependencies within the audio-visual input, allowing it to better capture subtle discrepancies between real and fake content. In our experiments, we evaluate ERF-BA-TFD+ on the DDL-AV dataset, which consists of both segmented and full-length video clips. Unlike previous benchmarks, which focused primarily on isolated segments, the DDL-AV dataset allows us to assess the model's performance in a more comprehensive and realistic setting. Our method achieves state-of-the-art results on this dataset, outperforming existing techniques in terms of both accuracy and processing speed. The ERF-BA-TFD+ model demonstrated its effectiveness in the "Workshop on Deepfake Detection, Localization, and Interpretability," Track 2: Audio-Visual Detection and Localization (DDL-AV), and won first place in this competition.

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