Efficiently classify synthesized facial images generated by different synthesis methods

Wei Li; Peng Qiao; Yong Dou

doi:10.1117/12.2589393

27 January 2021 Efficiently classify synthesized facial images generated by different synthesis methods

Wei Li, Peng Qiao, Yong Dou

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117200W (2021) https://doi.org/10.1117/12.2589393
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

With image generation and manipulation as one of impressive progress of convolutional neural networks (CNNs), facial image synthesis methods, e.g., DeepFakes, pose serious challenges to social and personal security. Specifically, we find that (1) CNN-based synthesized facial image detection methods generally fail to identify synthesized images generated by other synthesis methods; (2) classical detection methods exploiting one-class support vector machines (SVMs) and traditional features of video clips fail when only one image is available. In view of the above challenges, we propose and experimentally verify a method combining CNNs features and one-class SVMs, which not only effectively detects synthesized facial images generated by different methods, but also has good robustness to the variances of the scene content.

Citation Download Citation

Wei Li, Peng Qiao, and Yong Dou "Efficiently classify synthesized facial images generated by different synthesis methods", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117200W (27 January 2021); https://doi.org/10.1117/12.2589393

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