With Generative adversarial networks (GAN) achieving realistic image generation, fake image detection research has become an imminent need. In this paper, a novel detection algorithm is designed to exploit the structural defect in GAN, taking advantage of the most vulnerable link in GAN generators – the up-sampling process conducted by the Transposed Convolution operation. The Transposed Convolution in the process will cause the lack of global information in the generated images. Therefore, the Self-Attention mechanism is adopted correspondingly, equipping the algorithm with a much better comprehension of the global information than the other current work adopting pure CNN network, which is reflected in the significant increase in the detection accuracy. With the thorough comparison to the current work and corresponding careful analysis, it is verified that our proposed algorithm outperforms other current works in the field. Also, with experiments conducted on other image-generation categories and images undergone usual real-life post-processing methods, our proposed algorithm shows decent robustness for various categories of images under different reality circumstances, rather than restricted by image types and pure laboratory situation.

中文翻译：

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针对 GAN 生成器缺陷的具有自注意力机制的 GAN 生成图像检测

随着生成对抗网络（GAN）实现逼真的图像生成，假图像检测研究已成为迫在眉睫的需求。在本文中，设计了一种新的检测算法来利用 GAN 中的结构缺陷，利用 GAN 生成器中最脆弱的环节——转置卷积操作进行的上采样过程。过程中的转置卷积会导致生成的图像缺乏全局信息。因此，相应地采用了Self-Attention机制，使算法比目前采用纯CNN网络的其他工作更好地理解全局信息，这体现在检测精度的显着提高上。通过对当前工作的彻底对比和相应的仔细分析，经验证，我们提出的算法优于该领域的其他当前工作。此外，通过对其他图像生成类别和经过通常现实生活后处理方法的图像进行的实验，我们提出的算法在不同的现实环境下对各种类别的图像显示出不错的鲁棒性，而不受图像类型和纯实验室情况的限制。