Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection (2312.10461v2)

Abstract: 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.

• The paper introduces Neighboring Pixel Relationships (NPR) to reveal structural artifacts from up-sampling in CNN-based generative networks.
• It demonstrates an 11.6% performance boost in deepfake detection compared to traditional frequency-based methods.
• The study offers a framework for developing more generalizable detectors by emphasizing local pixel interdependencies in synthetic image generation.

Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection

This paper addresses a critical gap in the field of deepfake detection by focusing on the generator architectures within CNN-based networks, specifically examining the role of up-sampling operations in the creation of detectable synthetic artifacts. Unlike traditional deepfake detection methods that primarily target detection algorithm design, this paper investigates the generative processes themselves, revealing new insights into the structural artifact patterns introduced by up-sampling operators in GANs and diffusion models.

The authors propose the concept of Neighboring Pixel Relationships (NPR) to effectively characterize the artifacts resulting from up-sampling operations in synthetic image generation. By focusing on local interdependencies among pixels, NPR captures generalized structural artifacts that appear consistently across diverse generative frameworks. This is a notable shift from existing frequency-based approaches, which are often limited by the diversity of patterns present in the frequency domains of various GANs.

Experimental results reinforce the effectiveness of the NPR. Conducted on datasets comprising samples from 28 different generative models, the analysis shows a significant improvement, with NPR demonstrating an 11.6% performance increase over previous deepfake detection methods. This robust gain highlights the NPR's advantage in capturing invariant forgery artifacts, thus enhancing the ability of detectors to generalize to unseen deepfake sources.

The implications of this research are substantial. Practically, NPR provides a robust framework for enhancing the reliability and accuracy of deepfake detection systems across unseen synthetic sources. Theoretically, it suggests an alternative perspective on artifact analysis, urging researchers to explore localized pixel relationships over global artifact representations. This could potentially lead to more resilient detection systems, adaptable to the ever-expanding capacities of AI image synthesis technologies.

Looking forward, the paper speculates that further exploration of generator architectures could reveal additional invariant features for detection and inspire new detection models that utilize these insights. There is also the possibility of integrating NPR with other artifact representations to build more comprehensive detection systems. Such systems could leverage the strengths of various artifact types, ensuring robust detection capabilities across future and existing generative models.

In conclusion, this paper's reframing of up-sampling operations within CNN-based generators marks a valuable contribution to the deepfake detection literature, promising both immediate practical benefits and long-term theoretical insights for the field of AI-generated content verification.