This paper explores the task of detecting images generated by text-to-image diffusion models. To evaluate this, we consider images generated from captions in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable Diffusion and GLIDE. Our experiments show that it is possible to detect the generated images using simple Multi-Layer Perceptrons (MLPs), starting from features extracted by CLIP, or traditional Convolutional Neural Networks (CNNs). We also observe that models trained on images generated by Stable Diffusion can detect images generated by GLIDE relatively well, however, the reverse is not true. Lastly, we find that incorporating the associated textual information with the images rarely leads to significant improvement in detection results but that the type of subject depicted in the image can have a significant impact on performance. This work provides insights into the feasibility of detecting generated images, and has implications for security and privacy concerns in real-world applications. The code to reproduce our results is available at: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers

翻译：本文探讨了检测由文本到图像扩散模型生成图像的任务。为评估该任务，我们选取了MSCOCO和Wikimedia数据集中基于描述文本生成的两类图像，采用两种最先进的模型：Stable Diffusion和GLIDE。实验表明，通过CLIP提取的特征或传统卷积神经网络，利用简单的多层感知器即可检测生成图像。我们还发现，基于Stable Diffusion生成图像训练的模型能够较好地检测GLIDE生成的图像，但反之则不然。此外，将关联文本信息与图像结合对提升检测效果作用甚微，而图像所描绘的主体类型却对性能有显著影响。这项工作为检测生成图像的可行性提供了洞见，并对真实应用场景中的安全与隐私问题具有启示意义。复现结果的代码详见：https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers