AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinformation, the detection of AI-generated images is a pressing challenge and an active research topic. While many of the presented methods claim to achieve high detection accuracy, they are usually evaluated under idealized conditions. In particular, the adversarial robustness is often neglected, potentially due to a lack of awareness or the substantial effort required to conduct a comprehensive robustness analysis. In this work, we tackle this problem by providing a simpler means to assess the robustness of AI-generated image detectors. We present RAID (Robust evaluation of AI-generated image Detectors), a dataset of 72k diverse and highly transferable adversarial examples. The dataset is created by running attacks against an ensemble of seven state-of-the-art detectors and images generated by four different text-to-image models. Extensive experiments show that our methodology generates adversarial images that transfer with a high success rate to unseen detectors, which can be used to quickly provide an approximate yet still reliable estimate of a detector's adversarial robustnessOur findings indicate that current state-of-the-art AI-generated image detectors can be easily deceived by adversarial examples, highlighting the critical need for the development of more robust methods. We release our dataset at https://huggingface.co/datasets/aimagelab/RAID and evaluation code at https://github.com/pralab/RAID.

翻译：AI生成图像已达到人类无法可靠区分其与真实图像的质量水平。为应对欺诈和虚假信息的内在风险，检测AI生成图像已成为一项紧迫挑战和活跃的研究课题。尽管现有方法大多宣称能实现高检测精度，但它们通常在理想化条件下进行评估。特别是对抗鲁棒性常被忽视，这可能是由于缺乏认知或进行全面鲁棒性分析所需的大量工作。在本研究中，我们通过提供评估AI生成图像检测器鲁棒性的简化方法来解决该问题。我们提出RAID（AI生成图像检测器的鲁棒性评估数据集），这是一个包含7.2万个多样化且具有高迁移性的对抗样本数据集。该数据集通过针对七种最先进检测器组成的集成系统，以及四种不同文本到图像模型生成的图像进行攻击而创建。大量实验表明，我们的方法生成的对抗图像能以高成功率迁移到未见过的检测器，可用于快速提供检测器对抗鲁棒性的近似且可靠的估计。我们的研究结果表明，当前最先进的AI生成图像检测器极易被对抗样本欺骗，这凸显了开发更鲁棒方法的迫切需求。我们在https://huggingface.co/datasets/aimagelab/RAID发布数据集，并在https://github.com/pralab/RAID发布评估代码。