# Fake Generated Painting Detection via Frequency Analysis

arXiv: https://arxiv.org/abs/2003.02467 · HTML: https://mlsanity.com/paper/2003.02467/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Towards Generalizable and Robust Face Manipulation Detection via Bag-of-local-feature](https://mlsanity.com/paper/2103.07915/) (2021)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum](https://mlsanity.com/paper/2503.08484/) (2025)
- [Deepfake Detection and the Impact of Limited Computing Capabilities](https://mlsanity.com/paper/2402.14825/) (2024)

## Abstract (arXiv)

With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms.To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.