The generation of high-quality images has become widely accessible and is a rapidly evolving process. As a result, anyone can generate images that are indistinguishable from real ones. This leads to a wide range of applications, including malicious usage with deceptive intentions. Despite advances in detection techniques for generated images, a robust detection method still eludes us. Furthermore, model personalization techniques might affect the detection capabilities of existing methods. In this work, we utilize the architectural properties of convolutional neural networks (CNNs) to develop a new detection method. Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models. We tested the method on images produced by both Generative Adversarial Networks (GANs) and recent large text-to-image models (LTIMs) that rely on Diffusion Models. Our approach outperforms others trained under identical conditions and achieves comparable performance to state-of-the-art pre-trained detection methods on images generated by Stable Diffusion and MidJourney, with significantly fewer required train samples.

翻译：高质量图像的生成已变得广泛可及，且过程快速演进。因此，任何人都能生成与真实图像难以区分的假图像。这催生了大量应用，包括带有欺骗意图的恶意用途。尽管针对生成图像的检测技术已取得进展，但我们仍未掌握稳健的检测方法。此外，模型个性化技术可能影响现有方法的检测能力。本研究利用卷积神经网络的结构特性，开发了一种新型检测方法。该方法既能检测来自已知生成模型的图像，还能建立微调生成模型之间的关系。我们针对生成对抗网络及依赖扩散模型的最新大型文本到图像模型所产生的图像进行了测试。本方法在相同训练条件下优于其他方法，且在检测Stable Diffusion和MidJourney生成的图像时，能以显著更少的训练样本达到与最先进预训练检测方法相当的性能。