Leveraging Frequency Analysis for Deep Fake Image Recognition

TL;DR

This paper reveals that GAN-generated deep fake images contain consistent frequency domain artifacts caused by upsampling, which can be exploited to develop highly effective automated detection methods surpassing existing techniques.

Contribution

The study provides a comprehensive frequency domain analysis of GAN images, identifying artifacts caused by upsampling and proposing a novel detection approach that outperforms current methods.

Findings

GAN images show consistent frequency artifacts across architectures

Upsampling operations are the root cause of artifacts

Frequency-based detection surpasses state-of-the-art methods

Abstract

Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements have been largely made possible by Generative Adversarial Networks (GANs). While deep fake images have been thoroughly investigated in the image domain - a classical approach from the area of image forensics - an analysis in the frequency domain has been missing so far. In this paper, we address this shortcoming and our results reveal that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified. We perform a comprehensive analysis, showing that these artifacts are consistent across different neural network architectures, data sets, and resolutions. In a further investigation, we demonstrate that these artifacts are caused by upsampling operations found in all current GAN architectures, indicating a structural and fundamental problem in the way images are generated via GANs. Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.