The content discusses the importance of motion features in detecting face forgery, highlighting the limitations of current methods and proposing a new framework. The proposed method includes a motion consistency block and an anomaly detection block to improve generalizability and effectiveness in detecting manipulated faces across various datasets. Experimental results demonstrate the superiority of the proposed approach in both intra-domain and cross-domain evaluations.
The study emphasizes the significance of considering motion information in addition to appearance features for accurate face forgery detection. By introducing specialized blocks for motion consistency and anomaly detection, the authors aim to enhance the performance of existing video classification networks in identifying manipulated faces. The proposed framework shows promising results on popular face forgery datasets, showcasing its potential for real-world applications.
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arxiv.org
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by Jingyi Zhang... at arxiv.org 03-11-2024
https://arxiv.org/pdf/2403.05172.pdfDeeper Inquiries