Abstract: As a powerful tool for generating high-quality images, generative adversarial network (GAN) has been widely used in the field of image synthesis in recent years. However, with the rapid development of GAN technology, it also raises serious concerns about image forgery and fraud, especially in key areas such as news reporting, identity authentication and judicial forensics. These fake images not only are difficult to identify, but also may be used to spread false information, commit fraud, or even cause irreparable damage in legal cases. To cope with this challenge, researchers have proposed a variety of methods for detecting GAN-generated images, which can be mainly divided into feature-based methods and data-driven methods. This paper systematically sorts out the current main GAN image detection methods, and verifies their detection accuracy on different datasets through re-training experiments. Finally, the development trend of GAN image detection in the future is prospected, and potential research directions are proposed, in order to promote further innovation and development in this field.

Key words: generative adversarial network (GAN), deep learning, forgery detection

摘要： 生成对抗网络（generative adversarial network，GAN）作为生成高质量图像的强大工具，近年来在图像合成领域得到了广泛应用。然而，随着GAN技术的快速发展，引发了图像伪造和欺诈的严重担忧，特别是在新闻报道、身份认证以及司法取证等关键领域。这些伪造图像不仅难以辨别，还可能被用于传播虚假信息、实施诈骗，甚至在法律案件中造成难以弥补的损害。为应对这一挑战，研究者们提出了多种检测GAN生成图像的方法，主要可以分为基于特征识别的方法和基于数据驱动的方法。对于当前主要的GAN图像检测方法进行了系统梳理，并通过重训练实验验证了它们在不同数据集上的检测准确率。对未来GAN图像检测领域的发展趋势进行了展望，提出了潜在的研究方向，以推动该领域的进一步创新和发展。

关键词: 生成对抗网络（GAN）, 深度学习, 伪造检测