Abstract
Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photography devices or created by human artists (i.e., real images). While the outstanding performance of such generative models is generally well received, security concerns arise. For instance, such image generators could be used to facilitate fraud or scam schemes, generate and spread misinformation, or produce fabricated artworks. In this paper, we present a systematic attempt at understanding and detecting AI-generated images (AI-art) in adversarial scenarios. First, we collect and share a dataset of real images and their corresponding artificial counterparts generated by four popular AI image generators. The dataset, named ARIA, contains over 140K images in five categories: artworks (painting), social media images, news photos, disaster scenes, and anime pictures. This dataset can be used as a foundation to support future research on adversarial AI-art. Next, we present a user study that employs the ARIA dataset to evaluate if real-world users can distinguish with or without reference images. In a benchmarking study, we further evaluate if state-of-the-art open-source and commercial AI image detectors can effectively identify the images in the ARIA dataset. Finally, we present a ResNet-50 classifier and evaluate its accuracy and transferability on the ARIA dataset.
Abstract (translated)
生成式AI模型可以根据文本提示生成高质量的图像。生成的图像通常与传统光学摄影设备或由人类艺术家创建的图像很难区分（即真实图像）。虽然这种生成模型的突出表现通常得到了好评，但安全性问题也随之而来。例如，这种图像生成器可以用于促进诈骗或骗局，传播虚假信息或伪造艺术品。在本文中，我们提出了一个系统性的尝试，旨在理解和检测在对抗性场景中的人工生成图像（AI-艺术）。首先，我们收集并共享了由四个流行AI图像生成器生成的真实图像及其相应的人工对照物的数据集。这个数据集名为ARIA，包含超过14万张图像，分为五个类别：艺术品（绘画）、社交媒体图片、新闻照片、灾难场景和动漫图片。这个数据集可以作为支持未来研究对抗性AI-艺术的基础。接下来，我们进行了一个用户研究，使用ARIA数据集来评估现实世界的用户是否能够通过或不需要参考图像来区分真实图像。在基准测试中，我们进一步评估了最先进的开源和商业AI图像检测器是否能够有效识别ARIA数据集中的图像。最后，我们提出了一个ResNet-50分类器和评估其准确性及可转移性在ARIA数据集上的效果。
URL
https://arxiv.org/abs/2404.14581
