# A universal detector of CNN-generated images using properties of checkerboard artifacts in the frequency domain

arXiv: https://arxiv.org/abs/2108.01892 · HTML: https://mlsanity.com/paper/2108.01892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [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)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts](https://mlsanity.com/paper/1806.02658/) (2018)
- [CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection](https://mlsanity.com/paper/2012.00287/) (2020)
- [Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs](https://mlsanity.com/paper/2002.02117/) (2020)
- [Fake-image detection with Robust Hashing](https://mlsanity.com/paper/2102.01313/) (2021)

## Abstract (arXiv)

We propose a novel universal detector for detecting images generated by using CNNs. In this paper, properties of checkerboard artifacts in CNN-generated images are considered, and the spectrum of images is enhanced in accordance with the properties. Next, a classifier is trained by using the enhanced spectrums to judge a query image to be a CNN-generated ones or not. In addition, an ensemble of the proposed detector with emphasized spectrums and a conventional detector is proposed to improve the performance of these methods. In an experiment, the proposed ensemble is demonstrated to outperform a state-of-the-art method under some conditions.