Simultaneous forgery identification and localization in paintings using advanced correlation filters
With the availability of high resolution digital technology, there has been increased interest in developing statistical and image processing techniques that can enhance the existing capabilities of analyzing works of art for authenticity. This work explores the merits of using advanced correlation filters in supplementing art experts efforts in identifying forgeries among disputed paintings. We show that by training the optimal trade-off synthetic discriminant function (OTSDF) filter on each section of a coarsely parceled image of an original painting, we are not only able to distinguish between a low-quality digitized representation of a painting and its forgery, but also specifically indicate where the differences occur and where the replica is particularly faithful to the original. This method is also valuable in determining whether an original painting has undergone any modifications, given that a representation of the initial version is available.