Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms, an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generators, thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can, beyond frequency-based artifacts, produce generalized forgery artifacts. In particular, the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation, we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by \tft{28 distinct generative models}. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable \tft{12.8\%} improvement over existing methods. The code is available at https://github.com/chuangchuangtan/NPR-DeepfakeDetection.

通过对CNN-based generator架构的重新思考，揭示图像上采样操作产生的综合伪造图像中的伪造性特征，进而建立了合成伪造特征的广义表示。在开放世界数据集上的综合分析表明，本研究在现有方法上取得了12.8%的显著改进。

重新思考卷积神经网络生成网络中的上采样操作，用于可泛化的深度伪造检测