Detecting GAN-synthesized faces based on deep alignment network

Guihua Tang; Lei Sun; Xiuqing Mao

doi:10.1117/12.2579783

10 November 2020 Detecting GAN-synthesized faces based on deep alignment network

Guihua Tang, Lei Sun, Xiuqing Mao

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115840I (2020) https://doi.org/10.1117/12.2579783
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Generative adversary networks (GAN) have recently led to highly realistic synthesized image. For the current GAN-synthesized faces detection methods exist false prediction if the real faces with angles or occlusion. This paper proposes a GAN-synthesized faces detection method based on Deep Alignment Network (DAN), which improve prediction accuracy of real faces by makes the locations of facial landmark points more precise. Our method first uses DAN to obtain the locations of facial landmark points of real and synthesized faces; then the landmark points are converted into feature vectors by principal component analysis (PCA); finally, input feature vectors to the constructed Support Vector Machine (SVM)classifier for training. Experimental results show that our method achieves better performance than other method under face with angles or occlusion.

Citation Download Citation

Guihua Tang, Lei Sun, and Xiuqing Mao "Detecting GAN-synthesized faces based on deep alignment network", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115840I (10 November 2020); https://doi.org/10.1117/12.2579783

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KEYWORDS

Facial recognition systems

Data modeling

Principal component analysis

Image quality

Image resolution

Image processing

Neural networks

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