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Detecting GAN-generated face images via hybrid texture and sensor noise based features

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Abstract

Recent advances in Generative Adversarial Network (GAN) have made it considerably easy to generate photo-realistic images. However, when GAN-synthesized face images are used maliciously, they will lead to severe moral, ethical, and legal issues. To expose GAN-generated face images, most existing works rely heavily on deep models, which are costly and time-consuming. In this work, we propose a blind approach to detect GAN-generated face images by handcrafted features. Due to the differences of the inherent formation mechanism, nature and GAN-generated face images exhibit different texture and sensor noises, which are exploited as the clues to expose the GAN-generated face images. Specifically, uniform local binary pattern (LBP) features from real face and generated face images, and extract subtractive pixel adjacency matrix (SPAM) features in their sensor noises. Both features are fed to support vector machine (SVM) classifier to verify the authenticity of the face images. The result shows that our proposed approach can successfully detect GAN-generated fake face images with an accuracy up to 97.60%. Furthermore, it can distinguishing the fake face images generated by different GANs.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (61972143, 61972142), the Natural Science Foundation of Hunan Province, China (2020JJ4626) and the Scientific Research Foundation of Hunan Provincial Education Department of China (19B004).

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Authors and Affiliations

  1. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Tao Fu, Ming Xia & Gaobo Yang

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  1. Tao Fu
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  2. Ming Xia
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  3. Gaobo Yang
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Correspondence to Gaobo Yang.

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Fu, T., Xia, M. & Yang, G. Detecting GAN-generated face images via hybrid texture and sensor noise based features. Multimed Tools Appl 81, 26345–26359 (2022). https://doi.org/10.1007/s11042-022-12661-1

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  • DOI: https://doi.org/10.1007/s11042-022-12661-1

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