With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details in deep forgery samples, we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification. First, to address the critical problem in locating discriminative feature regions in fine-grained classification tasks, we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map. Second, using information complementation, we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches. Finally, we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels. We conduct sufficient ablation experiments and comparative experiments. The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++ and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years, which can achieve better detection results.
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