NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection

• URL: http://arxiv.org/abs/2306.06885v1
• Date: Mon, 12 Jun 2023 06:06:05 GMT
• Title: NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection
• Authors: Yu Chen, Yang Yu, Rongrong Ni, Yao Zhao, Haoliang Li
• Abstract summary: Existing multimodal detection methods capture audio-visual inconsistencies to expose Deepfake videos. We propose a novel Deepfake detection method to mine the correlation between Non-critical Phonemes and Visemes, termed NPVForensics. Our model can be easily adapted to the downstream Deepfake datasets with fine-tuning.
• Score: 50.33525966541906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deepfake technologies empowered by deep learning are rapidly evolving, creating new security concerns for society. Existing multimodal detection methods usually capture audio-visual inconsistencies to expose Deepfake videos. More seriously, the advanced Deepfake technology realizes the audio-visual calibration of the critical phoneme-viseme regions, achieving a more realistic tampering effect, which brings new challenges. To address this problem, we propose a novel Deepfake detection method to mine the correlation between Non-critical Phonemes and Visemes, termed NPVForensics. Firstly, we propose the Local Feature Aggregation block with Swin Transformer (LFA-ST) to construct non-critical phoneme-viseme and corresponding facial feature streams effectively. Secondly, we design a loss function for the fine-grained motion of the talking face to measure the evolutionary consistency of non-critical phoneme-viseme. Next, we design a phoneme-viseme awareness module for cross-modal feature fusion and representation alignment, so that the modality gap can be reduced and the intrinsic complementarity of the two modalities can be better explored. Finally, a self-supervised pre-training strategy is leveraged to thoroughly learn the audio-visual correspondences in natural videos. In this manner, our model can be easily adapted to the downstream Deepfake datasets with fine-tuning. Extensive experiments on existing benchmarks demonstrate that the proposed approach outperforms state-of-the-art methods.

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