Recently, the proliferation of increasingly realistic synthetic images generated by various generative adversarial networks has increased the risk of misuse. Consequently, there is a pressing need to develop a generalizable detector for accurately recognizing fake images. The conventional methods rely on generating diverse training sources or large pretrained models. In this work, we show that, on the contrary, the small and training-free filter is sufficient to capture more general artifact representations. Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources. In our framework, handcrafted filters and the randomly-initialized convolutional layer can be used as the training-free artifact representations extractor with excellent results. With the data-independent operator of a popular classifier, such as Resnet50, one could already reach a new state-of-the-art without bells and whistles. We evaluate the effectiveness of the DIO on 33 generation models, even DALLE and Midjourney. Our detector achieves a remarkable improvement of $13.3\%$, establishing a new state-of-the-art performance. The DIO and its extension can serve as strong baselines for future methods. The code is available at \url{https://github.com/chuangchuangtan/Data-Independent-Operator}.

翻译：近期，各类生成对抗网络生成的逼真合成图像日益泛滥，加剧了其被滥用的风险。因此，迫切需要开发一种可泛化的检测器来准确识别伪造图像。传统方法依赖生成多样化的训练数据或使用大型预训练模型。然而，本研究表明，小而无需训练的滤波器足以捕获更通用的人工痕迹表征。由于其对训练源和测试源均无偏倚，我们将其定义为与数据无关的算子（Data-Independent Operator, DIO），以在未见过的数据源上实现显著的性能提升。在我们的框架中，手工设计的滤波器和随机初始化的卷积层可作为无训练的人工痕迹提取器，并取得优异效果。通过将这种与数据无关的算子应用于流行的分类器（如ResNet50），无需额外复杂技术即可达到新的最优性能。我们在33个生成模型（包括DALL·E和Midjourney）上验证了DIO的有效性。我们的检测器实现了13.3%的显著提升，刷新了最优性能记录。DIO及其扩展可为未来方法提供强大的基线。代码已开源在 \url{https://github.com/chuangchuangtan/Data-Independent-Operator}。