Journal
PROCEEDINGS OF THE 6TH ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY (IH&MMSEC'18)
Volume -, Issue -, Pages 43-47Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3206004.3206009
Keywords
Image Forensics; Deep Learning; Generative Adversarial Networks (GAN); Convolutional Neural Network (CNN)
Funding
- NSFC [61672551]
- Special Research Plan of Guangdong Province [2015TQ01X365]
- Guangzhou Science and Technology Plan Project [201707010167]
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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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