JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 415
Type: Topic Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract - #309273
Title: Forgery Detection in Paintings
Author(s): Yi Wang*+ and Ingrid Daubechies and Gungor Polatkan and Sina Jafarpour
Companies: SAMSI/Duke University and Duke University and Princeton University and Yahoo! Research
Keywords: image processing ; wavelet ; machine learning ; forgery detection ; hidden-markov-tree ; supervised learning

This work studies how machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or doubtful origin. Previous work has shown that variation in image clarity in the experimental data sets was correlated with authenticity, and may have acted as a confounding factor, artificially improving the results. Therefore, a data set with ground truth and uniform acquisition conditions is provided to determine the extent of this factor's influence. While many previously successful methods turn out to be ineffective, supervised machine learning on features extracted from Hidden-Markov-Tree modeling of the paintings' wavelet coefficients demonstrates its potential to distinguish copies from originals in this data set. In order to further study and improve this approach, a larger data set is created under similar conditions. In addition, more careful analysis and experiments are conducted to provide new insights.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2013 program

2013 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.