TY - GEN
T1 - Fake Generated Painting Detection Via Frequency Analysis
AU - Bai, Yong
AU - Guo, Yuanfang
AU - Wei, Jinjie
AU - Lu, Lin
AU - Wang, Rui
AU - Wang, Yunhong
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61802391 and Grant 61421003, in part by the Fundamental Research Funds for Central Universities. The corresponding author is Yuanfang Guo. (Email: andyguo@buaa.edu.cn)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85098666930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098666930&partnerID=8YFLogxK
U2 - 10.1109/ICIP40778.2020.9190892
DO - 10.1109/ICIP40778.2020.9190892
M3 - Conference contribution
AN - SCOPUS:85098666930
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1256
EP - 1260
BT - 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PB - IEEE Computer Society
T2 - 2020 IEEE International Conference on Image Processing, ICIP 2020
Y2 - 25 September 2020 through 28 September 2020
ER -