Fake Generated Painting Detection Via Frequency Analysis

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.

[1]  E. Valuations.  A REVIEW ON EVALUATION METRICS FOR DATA CLASSIFICATION EVALUATIONS , 2015 .

[2]  Hyunsoo Kim,et al.  Learning to Discover Cross-Domain Relations with Generative Adversarial Networks , 2017, ICML.

[3]  Wei Zhang,et al.  Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning , 2018, Sensors.

[4]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[5]  Xiaokang Yang,et al.  Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer , 2019, IEEE Transactions on Image Processing.

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[8]  Jun Wang,et al.  Attention-Aware Multi-Stroke Style Transfer , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Andrea Vedaldi,et al.  Texture Networks: Feed-forward Synthesis of Textures and Stylized Images , 2016, ICML.

[10]  Bin Li,et al.  Identification of deep network generated images using disparities in color components , 2020, Signal Process..

[11]  Mark W. Schmidt,et al.  Fast Patch-based Style Transfer of Arbitrary Style , 2016, ArXiv.

[12]  David Haussler,et al.  Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.

[13]  Xiaochun Cao,et al.  Fake Colorized Image Detection , 2018, IEEE Transactions on Information Forensics and Security.

[14]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Bin Li,et al.  Detection of Deep Network Generated Images Using Disparities in Color Components , 2018, ArXiv.

[16]  Baining Guo,et al.  Face X-Ray for More General Face Forgery Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Michael Isard,et al.  Lost in quantization: Improving particular object retrieval in large scale image databases , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Luisa Verdoliva,et al.  Do GANs Leave Artificial Fingerprints? , 2018, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[21]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[22]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[23]  Thomas Mensink,et al.  Image Classification with the Fisher Vector: Theory and Practice , 2013, International Journal of Computer Vision.

[24]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[25]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[26]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[27]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .