Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models. Despite their efficacy, there is a dearth of research dedicated to detecting diffusion-generated images, which could pose potential security and privacy risks. This paper addresses this gap by proposing a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID). Comprising statistical-based $\text{SeDID}_{\text{Stat}}$ and neural network-based $\text{SeDID}_{\text{NNs}}$, SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising computation errors. Our evaluations demonstrate SeDID's superior performance over existing methods when applied to diffusion models. Thus, our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.

翻译：图像合成随着基于扩散的生成模型（如去噪扩散概率模型DDPM和文生图扩散模型）的出现取得了显著进展。尽管这些模型效果显著，但针对扩散生成图像检测的研究却十分缺乏，这可能导致潜在的安全和隐私风险。本文通过提出一种名为“扩散生成图像检测的逐步误差法（SeDID）”的新型检测方法，填补了这一空白。SeDID包括基于统计的SeDID_Stat和基于神经网络的SeDID_NNs，它利用了扩散模型的独特属性，即确定性逆向和确定性去噪计算误差。我们的评估表明，当应用于扩散模型时，SeDID的性能优于现有方法。因此，我们的工作在区分扩散模型生成的图像方面做出了关键贡献，标志着人工智能安全领域的重要一步。