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- W4206087679 abstract "AbstractAs deep learning technology develops rapidly, the adverse impact of counterfeiting with deep learning is expanding. It has become a hot research topic to explore ways of detecting deepfakes, i.e., fake images or videos generated with deep learning methods. The results reported in the literature show that many existing detection methods deteriorate severely in cross-dataset tests. To solve this problem, we propose a novel network that embodies three key features: 1) double-stream feature extraction module to reveal artifacts simultaneously from the original image space and the noise residual space; 2) temporal self-attention module to infer the authenticity of the current frame from the temporal context; 3) tampering region segmentation module that is based on fully convolutional network and predicts the tampered face area in each frame. We also propose a modified IoU (Intersection-over-Union) measurement of the predicted tampering region against the face region, upon which the authenticity of a frame is determined. We conduct comprehensive comparative experiments on five major datasets and demonstrate the superiority of our proposed model, particularly in the challenging cross-dataset settings.KeywordsDeepfake video detectionImage segmentation networkTemporal self-attention mapDouble-stream feature extraction" @default.
- W4206087679 created "2022-01-26" @default.
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- W4206087679 date "2022-01-01" @default.
- W4206087679 modified "2023-10-17" @default.
- W4206087679 title "Double-Stream Segmentation Network with Temporal Self-attention for Deepfake Video Detection" @default.
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- W4206087679 doi "https://doi.org/10.1007/978-3-030-95398-0_3" @default.
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