Detection of forgery in paintings using supervised learning

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 s...

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Bibliographic Details
Main Authors: Polatkan, G., Jafarpour, S., Brasoveanu, A., Hughes, S., Daubechies, I.
Format: Conference Proceeding
Language:English
Subjects:
Blur Identification
Digital Painting Analysis
Forgery
Forgery Detection
Hidden Markov models
Hidden Markov Trees
Image analysis
Image Classification
Image color analysis
Machine learning
Statistics
Supervised learning
Wavelet coefficients
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Summary: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.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5413338