# Detecting High-Quality GAN-Generated Face Images using Neural Networks

arXiv: https://arxiv.org/abs/2203.01716 · HTML: https://mlsanity.com/paper/2203.01716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 3 Mar 2022.

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

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [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)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [Detecting GAN-generated Imagery using Color Cues](https://mlsanity.com/paper/1812.08247/) (2018)
- [Incremental learning for the detection and classification of GAN-generated images](https://mlsanity.com/paper/1910.01568/) (2019)
- [Exposing GAN-synthesized Faces Using Landmark Locations](https://mlsanity.com/paper/1904.00167/) (2019)
- [One-Shot GAN Generated Fake Face Detection](https://mlsanity.com/paper/2003.12244/) (2020)

## Built on by (most cited first, 3 of 3)

- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [Smudged Fingerprints: A Systematic Evaluation of the Robustness of AI Image Fingerprints](https://mlsanity.com/paper/2512.11771/) (2025)
- [Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents](https://mlsanity.com/paper/2304.02982/) (2023)

## Abstract (arXiv)

In the past decades, the excessive use of the last-generation GAN (Generative Adversarial Networks) models in computer vision has enabled the creation of artificial face images that are visually indistinguishable from genuine ones. These images are particularly used in adversarial settings to create fake social media accounts and other fake online profiles. Such malicious activities can negatively impact the trustworthiness of users identities. On the other hand, the recent development of GAN models may create high-quality face images without evidence of spatial artifacts. Therefore, reassembling uniform color channel correlations is a challenging research problem. To face these challenges, we need to develop efficient tools able to differentiate between fake and authentic face images. In this chapter, we propose a new strategy to differentiate GAN-generated images from authentic images by leveraging spectral band discrepancies, focusing on artificial face image synthesis. In particular, we enable the digital preservation of face images using the Cross-band co-occurrence matrix and spatial co-occurrence matrix. Then, we implement these techniques and feed them to a Convolutional Neural Networks (CNN) architecture to identify the real from artificial faces. Additionally, we show that the performance boost is particularly significant and achieves more than 92% in different post-processing environments. Finally, we provide several research observations demonstrating that this strategy improves a comparable detection method based only on intra-band spatial co-occurrences.