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{11.6\%} improvement over existing methods. The code is available at https://github.com/chuangchuangtan/NPR-DeepfakeDetection.

翻译：最近，通过多种生成对抗网络（GAN）和扩散模型生成的高度逼真合成图像的激增，显著加剧了被滥用的风险。尽管深度伪造检测的主要焦点传统上集中在检测算法的设计上，但对生成器架构的探究性研究近年来却明显缺失。本文通过重新思考基于CNN的生成器架构，弥补了这一空白，从而建立了合成伪影的通用表示。我们的发现表明，上采样算子除了产生基于频率的伪影外，还能生成广义的伪造伪影。特别是，由上采样算子引起的图像像素之间的局部相互依赖性在GAN或扩散生成的合成图像中显著体现。基于这一观察，我们引入了邻域像素关系（NPR）的概念，以捕获和表征由上采样操作产生的通用结构伪影。我们在一个开放世界数据集上进行了全面分析，该数据集包含由\texttft{28种不同生成模型}生成的样本。该分析最终确立了新的最先进性能，展示了相比现有方法\texttft{11.6\%}的显著改进。代码可在https://github.com/chuangchuangtan/NPR-DeepfakeDetection获取。