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Detecting Generated Images by Real Images

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

The widespread of generative models have called into question the authenticity of many things on the web. In this situation, the task of image forensics is urgent. The existing methods examine generated images and claim a forgery by detecting visual artifacts or invisible patterns, resulting in generalization issues. We observed that the noise pattern of real images exhibits similar characteristics in the frequency domain, while the generated images are far different. Therefore, we can perform image authentication by checking whether an image follows the patterns of authentic images. The experiments show that a simple classifier using noise patterns can easily detect a wide range of generative models, including GAN and flow-based models. Our method achieves state-of-the-art performance on both low- and high-resolution images from a wide range of generative models and shows superior generalization ability to unseen models. The code is available at https://github.com/Tangsenghenshou/Detecting-Generated-Images-by-Real-Images.

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Notes

  1. 1.

    http://www.grip.unina.it/download/DoGANs/.

  2. 2.

    http://www.seeprettyface.com/information.html.

  3. 3.

    https://github.com/imlixinyang/HiSD.

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Correspondence to Xiuli Bi or Bin Xiao .

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Liu, B., Yang, F., Bi, X., Xiao, B., Li, W., Gao, X. (2022). Detecting Generated Images by Real Images. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-19781-9_6

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