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arXiv · 1804.04418

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

Huy H. Nguyen·Ngoc-Dung T. Tieu·Hoang-Quoc Nguyen-Son·Junichi Yamagishi·Isao Echizen
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Abstract

Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security. While most approaches focus on the image rendering phase, this paper presents a method based on increasing the naturalness of CG facial images from the perspective of spoofing detectors. The proposed method is implemented using a convolutional neural network (CNN) comprising two autoencoders and a transformer and is trained using a black-box discriminator without gradient information. Over 50% of the transformed CG images were not detected by three state-of-the-art spoofing detectors. This capability raises an alarm regarding the reliability of facial authentication systems, which are becoming widely used in daily life.

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Huy H. Nguyen, Ngoc-Dung T. Tieu, Hoang-Quoc Nguyen-Son, Junichi Yamagishi, Isao Echizen. 2018-04-12. Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector. https://doi.org/10.1109/icme.2018.8486579

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