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http://dx.doi.org/10.9717/kmms.2019.22.12.1447

A StyleGAN Image Detection Model Based on Convolutional Neural Network  

Kim, Jiyeon (Center for Software Educational Innovation, Seoul Women's University)
Hong, Seung-Ah (Dept. of Information Security, Seoul Women's University)
Kim, Hamin (Dept. of Information Security, Seoul Women's University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.12, 2019 , pp. 1447-1456 More about this Journal
Abstract
As artificial intelligence technology is actively used in image processing, it is possible to generate high-quality fake images based on deep learning. Fake images generated using GAN(Generative Adversarial Network), one of unsupervised learning algorithms, have reached levels that are hard to discriminate from the naked eye. Detecting these fake images is required as they can be abused for crimes such as illegal content production, identity fraud and defamation. In this paper, we develop a deep-learning model based on CNN(Convolutional Neural Network) for the detection of StyleGAN fake images. StyleGAN is one of GAN algorithms and has an excellent performance in generating face images. We experiment with 48 number of experimental scenarios developed by combining parameters of the proposed model. We train and test each scenario with 300,000 number of real and fake face images in order to present a model parameter that improves performance in the detection of fake faces.
Keywords
Deep Learning; Generative Adversarial Network; Convolutional Neural Network; Fake Image Detection; Face Detection;
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Times Cited By KSCI : 2  (Citation Analysis)
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