In recent years, diffusion models have become one of the main methods for generating images. However, detecting images generated by these models remains a challenging task. This paper proposes a novel method for detecting images generated by Latent Diffusion Models (LDM) by identifying artifacts introduced by their autoencoders. By training a detector to distinguish between real images and those reconstructed by the LDM autoencoder, the method enables detection of generated images without directly training on them. The novelty of this research lies in the fact that, unlike similar approaches, this method does not require training on synthesized data, significantly reducing computational costs and enhancing generalization ability. Experimental results show high detection accuracy with minimal false positives, making this approach a promising tool for combating fake images.

翻译：近年来，扩散模型已成为图像生成的主要方法之一。然而，检测这些模型生成的图像仍然是一项具有挑战性的任务。本文提出了一种通过识别潜在扩散模型（LDM）自编码器引入的伪影来检测LDM生成图像的新方法。通过训练一个检测器来区分真实图像与LDM自编码器重建的图像，该方法能够在无需直接对生成图像进行训练的情况下实现检测。本研究的创新之处在于，与类似方法不同，该方法无需在合成数据上进行训练，从而显著降低了计算成本并增强了泛化能力。实验结果表明，该方法具有较高的检测精度和极低的误报率，使其成为应对伪造图像的一种有前景的工具。