Fake Generated Painting Detection Via Frequency Analysis

Yong Bai, Yuanfang Guo, Jinjie Wei, Lin Lu, Rui Wang, Yunhong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages1256-1260
Number of pages5
ISBN (Electronic)9781728163956
DOIs
StatePublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: Sep 25 2020Sep 28 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period9/25/209/28/20

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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