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# Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
[](https://github.com/chuangchuangtan/NPR-DeepfakeDetection)
[](https://arxiv.org/abs/2312.10461)
Original Paper:
[Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection](https://arxiv.org/abs/2312.10461).
Authors: Chuangchuang Tan, Huan Liu, Yao Zhao, Shikui Wei, Guanghua Gu, Ping Liu, Yunchao Wei.
## Abstract
Recently, the proliferation of highly realistic synthetic
images, facilitated through a variety of GANs and Diffu-
sions, has significantly heightened the susceptibility to mis-
use. While the primary focus of deepfake detection has tra-
ditionally 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 generator, thereby establishing a generalized
representation of synthetic artifacts. Our findings illumi-
nate 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 demon-
strated 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 cap-
ture 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 28 distinct generative models. This
analysis culminates in the establishment of a novel state-of-
the-art performance, showcasing a remarkable 11.6% im-
provement over existing methods
## Please Cite
```
@inproceedings{tan2024rethinking,
title={Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection},
author={Tan, Chuangchuang and Zhao, Yao and Wei, Shikui and Gu, Guanghua and Liu, Ping and Wei, Yunchao},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={28130--28139},
year={2024}
}
``` |