Abstract
Face manipulation techniques have raised concern over potential threats, which demand effective images forensic methods. Various approaches have been proposed, but when detecting higher-quality manipulated faces, the performance of previous method is not good enough. To prevent the abuse of these techniques and improve the detection ability, this paper proposes a new algorithm named Squeeze-Excitation Euclidean Distance Network (SE_EDNet) to detect manipulated faces, which is suitable for Deepfakes and GANs detection. SE_EDNet use Euclidean distance to describe similaity of vectors, which gives higher weights to important areas than traditional self-attention mechanism. Further, we take frequency into account and extract residuals information, which are obtained by a second-order filter. Then residuals are combined with original images as the input features for the network. Comparison experiment shows SE_EDNet performs better than existing algorithms. Extensive robustness experiments on Celeb-DF and DFFD demonstrate that proposed algorithm is robust against attacking on AUC scores and Recalls.
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The work is suppoted by the BAIDU supports Ministry of Education’s Education Cooperation Program(No. 2012115PCK00690).
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Peng, C., Yao, L., Sun, T., Jiang, X., Mi, Z. (2021). SE_EDNet: A Robust Manipulated Faces Detection Algorithm. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_6
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DOI: https://doi.org/10.1007/978-3-030-89029-2_6
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