Fake Generated Painting Detection via Frequency Analysis
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 1; 2025: 1.
6 papers.
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- 2025Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum11 Mar 2025
- 2024Deepfake Detection and the Impact of Limited Computing Capabilities8 Feb 2024
- 2022Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection12 Dec 2022
- 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspective20 Nov 2022
- 2021Towards Generalizable and Robust Face Manipulation Detection via Bag-of-local-feature14 Mar 2021
- 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizon27 Feb 2021