计算机科学
卷积神经网络
人工智能
面子(社会学概念)
鉴定(生物学)
图像(数学)
生成语法
模式识别(心理学)
面部识别系统
对抗制
功能(生物学)
机器学习
植物
进化生物学
社会科学
生物
社会学
作者
Huaxiao Mo,Bolin Chen,Weiqi Luo
标识
DOI:10.1145/3206004.3206009
摘要
Generative Adversarial Network (GAN) is a prominent generative model that are widely used in various applications. Recent studies have indicated that it is possible to obtain fake face images with a high visual quality based on this novel model. If those fake faces are abused in image tampering, it would cause some potential moral, ethical and legal problems. In this paper, therefore, we first propose a Convolutional Neural Network (CNN) based method to identify fake face images generated by the current best method [20], and provide experimental evidences to show that the proposed method can achieve satisfactory results with an average accuracy over 99.4%. In addition, we provide comparative results evaluated on some variants of the proposed CNN architecture, including the high pass filter, the number of the layer groups and the activation function, to further verify the rationality of our method.
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