Abstract
Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by eight diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code and dataset are available at this https URL.
Abstract (translated)
扩散模型在视觉合成方面取得了显著成功,但也引发了对于可能用于恶意目的滥用的担忧。在本文中,我们寻求建立一个探测器来分辨出真实的图像和扩散生成图像。我们发现,现有的探测器很难检测扩散模型生成的图像,即使我们将从特定扩散模型生成的图像在训练数据中 included 进去。为了解决这一问题,我们提出了一种新图像表示,称为扩散重建错误(DIRE),它通过预先训练的扩散模型测量输入图像和其重建副本之间的误差。我们观察到,扩散生成图像可以近似地由扩散模型重建,而真实的图像却无法。这提供了一个暗示,DIRE可以作为区分生成和真实图像的桥。DIRE提供了检测大多数扩散模型生成的图像的有效方法,并且对于从未曾见过的扩散模型生成的图像和对各种干扰的鲁棒性都适用。此外,我们建立了一个包括八个扩散模型生成的图像的全面扩散生成基准,以评估扩散生成图像探测器的性能。我们对收集的基准进行广泛的实验,证明了DIRE比先前生成的图像探测器表现更好。代码和数据集在本 https URL 上可用。
URL
https://arxiv.org/abs/2303.09295
