# Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images

arXiv: https://arxiv.org/abs/2112.08050 · HTML: https://mlsanity.com/paper/2112.08050/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [Adversarial Latent Autoencoders](https://mlsanity.com/paper/2004.04467/) (2020)
- [Fourier Spectrum Discrepancies in Deep Network Generated Images](https://mlsanity.com/paper/1911.06465/) (2019)
- [Spatial Frequency Bias in Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/2010.01473/) (2020)

## Built on by (most cited first, 9 of 9)

- [Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning](https://mlsanity.com/paper/2309.05911/) (2023)
- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [Why Do Facial Deepfake Detectors Fail?](https://mlsanity.com/paper/2302.13156/) (2023)
- [Multimodal Graph Learning for Deepfake Detection](https://mlsanity.com/paper/2209.05419/) (2022)
- [UGAD: Universal Generative AI Detector utilizing Frequency Fingerprints](https://mlsanity.com/paper/2409.07913/) (2024)
- [LLMs Are Not Yet Ready for Deepfake Image Detection](https://mlsanity.com/paper/2506.10474/) (2025)
- [Generative Adversarial Networks and Other Generative Models](https://mlsanity.com/paper/2207.03887/) (2022)
- [FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection](https://mlsanity.com/paper/2509.16602/) (2025)
- [Seeing Through the Blur: Unlocking Defocus Maps for Deepfake Detection](https://mlsanity.com/paper/2509.23289/) (2025)

## Abstract (arXiv)

The rapid progression of Generative Adversarial Networks (GANs) has raised a concern of their misuse for malicious purposes, especially in creating fake face images. Although many proposed methods succeed in detecting GAN-based synthetic images, they are still limited by the need for large quantities of the training fake image dataset and challenges for the detector's generalizability to unknown facial images. In this paper, we propose a new approach that explores the asynchronous frequency spectra of color channels, which is simple but effective for training both unsupervised and supervised learning models to distinguish GAN-based synthetic images. We further investigate the transferability of a training model that learns from our suggested features in one source domain and validates on another target domains with prior knowledge of the features' distribution. Our experimental results show that the discrepancy of spectra in the frequency domain is a practical artifact to effectively detect various types of GAN-based generated images.