  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.
