DocumentCode :
3472559
Title :
Detection of forgery in paintings using supervised learning
Author :
Polatkan, Güngör ; Jafarpour, Sina ; Brasoveanu, Andrei ; Hughes, Shannon ; Daubechies, Ingrid
Author_Institution :
Depts. of Electr. Eng., Princeton Univ., Princeton, NJ, USA
fYear :
2009
fDate :
7-10 Nov. 2009
Firstpage :
2921
Lastpage :
2924
Abstract :
This paper examines whether machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or disputed paintings. Recent work on this topic has presented some promising initial results. Our reexamination of some of these recently successful experiments shows that variations in image clarity in the experimental datasets were correlated with authenticity, and may have acted as a confounding factor, artificially improving the results. To determine the extent of this factor´s influence on previous results, we provide a new ¿ground truth¿ data set in which originals and copies are known and image acquisition conditions are uniform. Multiple previously-successful methods are found ineffective on this new confounding-factor-free dataset, but we demonstrate that supervised machine learning on features derived from hidden-Markov-tree-modeling of the paintings´ wavelet coefficients has the potential to distinguish copies from originals in the new dataset.
Keywords :
art; hidden Markov models; image classification; learning (artificial intelligence); object detection; wavelet transforms; authentication; confounding-factor-free dataset; forgery detection; ground truth data set; hidden Markov tree modeling; image acquisition; image analysis tools; image classification; image painting; painting wavelet coefficients; supervised machine learning; Art; Forgery; Hidden Markov models; Image analysis; Image color analysis; Machine learning; Painting; Statistics; Supervised learning; Wavelet coefficients; Blur Identification; Digital Painting Analysis; Forgery Detection; Hidden Markov Trees; Image Classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
ISSN :
1522-4880
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2009.5413338
Filename :
5413338
Link To Document :
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