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：本文探讨了检测文本到图像扩散模型生成的图像的任务。为了评估这一任务，我们考虑使用两种最先进的模型：稳定扩散和GLIDE，从MSCOCO和维基百科数据集中生成的图片。我们的实验表明，可以使用简单的多层感知机（MLPs）或传统的卷积神经网络（CNNs）来检测生成的图像，开始时使用由CLIP提取的特征。我们还观察到，对于在稳定扩散生成的图像进行训练的模型可以相对较好地检测由GLIDE生成的图像，但反之则不然。最后，我们发现，将与图像相关联的文本信息包括在内很少会显著提高检测结果，但所描绘的主题类型对性能有明显影响。这项工作提供了检测生成图像可行性的见解，并对实际应用中的安全和隐私问题具有影响。我们的代码可以在以下链接中获得: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers