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ICIP
2009
IEEE

Detection Of Forgery In Paintings Using Supervised Learning

15 years 1 months ago
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 [1] 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 HiddenMarkov-Tree-modeling of the paintings' wavelet coefficients has the potential to distinguish copies from ...
Added 10 Nov 2009
Updated 26 Dec 2009
Type Conference
Year 2009
Where ICIP
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