The widespread adoption of generative image models has highlighted the urgent need to detect artificial content, which is a crucial step in combating widespread manipulation and misinformation. Consequently, numerous detectors and associated datasets have emerged. However, many of these datasets inadvertently introduce undesirable biases, thereby impacting the effectiveness and evaluation of detectors. In this paper, we emphasize that many datasets for AI-generated image detection contain biases related to JPEG compression and image size. Using the GenImage dataset, we demonstrate that detectors indeed learn from these undesired factors. Furthermore, we show that removing the named biases substantially increases robustness to JPEG compression and significantly alters the cross-generator performance of evaluated detectors. Specifically, it leads to more than 11 percentage points increase in cross-generator performance for ResNet50 and Swin-T detectors on the GenImage dataset, achieving state-of-the-art results. We provide the dataset and source codes of this paper on the anonymous website: https://www.unbiased-genimage.org

该研究讨论了使用生成图像模型检测人工内容的紧迫性，并指出当前的数据集中存在与JPEG压缩和图像大小相关的偏差。研究还展示了去除这些偏差对JPEG压缩的鲁棒性和评估检测器的不同生成器间性能的显著影响，其中ResNet50和Swin-T检测器在GenImage数据集上的不同生成器间性能提高了超过11个百分点，达到了最先进的结果。

揭示生成图像检测数据集中的常见偏差：伪造还是JPEG？