Abstract
It has become increasingly challenging to distinguish real faces from their visually realistic fake counterparts, due to the great advances of deep learning based face manipulation techniques in recent years. In this paper, we introduce a deep learning method to detect face manipulation. It consists of two stages: feature extraction and binary classification. To better distinguish fake faces from real faces, we resort to the triplet loss function in the first stage. We then design a simple linear classification network to bridge the learned contrastive features with the real/fake faces. Experimental results on public benchmark datasets demonstrate the effectiveness of this method, and show that it generates better performance than state-of-the-art techniques in most cases.
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Acknowledgement
This work is supported in part by Deakin University (Australia) internal grant (CY01-251301-F003-PJ03906-PG00447 and 251301-F003-PG00216-PJ03906).
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Feng, D., Lu, X., Lin, X. (2020). Deep Detection for Face Manipulation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_37
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DOI: https://doi.org/10.1007/978-3-030-63823-8_37
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