Visible to the public Fake Generated Painting Detection Via Frequency Analysis

TitleFake Generated Painting Detection Via Frequency Analysis
Publication TypeConference Paper
Year of Publication2020
AuthorsBai, Y., Guo, Y., Wei, J., Lu, L., Wang, R., Wang, Y.
Conference Name2020 IEEE International Conference on Image Processing (ICIP)
KeywordsDatabases, feature extraction, Forgery, Fourier transform, frequency analysis, frequency-domain analysis, image forgery detection, neural style transfer, Painting, Paintings, Predictive Metrics, pubcrawl, Resiliency, Scalability, style transfer, Support vector machines, Testing
AbstractWith 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.
Citation Keybai_fake_2020