Abstract
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.
Abstract (translated)
近年来，扩散模型已成为生成图像的主要方法之一。然而，检测这些模型生成的图像仍然是一个具有挑战性的任务。本文提出了一种新的方法，通过识别由潜在扩散模型（LDM）自动编码器引入的伪影来检测其生成的图像。该方法通过对真实图像和LDM自动编码器重建的图像进行区分训练探测器，从而能够在不直接对生成图像进行训练的情况下检测到它们。这项研究的新颖之处在于，与类似的方法不同，这种方法不需要在合成数据上进行训练，大大降低了计算成本并增强了泛化能力。实验结果显示了高检测准确率和极低的误报率，使得这一方法成为对抗假图像的一种有前景的工具。
URL
https://arxiv.org/abs/2411.06441
