Open AccessForensic Imaging for Art Diagnostics. What Evidence Should We Trust?
Author(s) -
Anna Pelagotti,
Alessandro Piva,
Francesca Uccheddu,
Dasara Shullani,
Maria Francesca Alberghina,
Salvatore Schiavone,
Emanuela Massa,
C M Menchetti
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/949/1/012076
Subject(s) - computer science , artificial intelligence , digital imaging , state of art , painting , state (computer science) , computer forensics , computer vision , digital image , digital forensics , image (mathematics) , data science , image processing , computer security , art , algorithm , visual arts
Diagnostics digital images are often used to assess artworks. However, as all digital images they are also concerned by the issue of integrity. Computer vision techniques can be employed to obtain physical evidence of possible tampering. In this paper we explore the possibility to apply state of the art forensic algorithms to typical painting diagnostic images, taking into consideration real case studies. State of the art algorithms have been applied to genuine and modified diagnostic images to detect if, and how, forgeries of such images could be automatically detected and documented. To the best of our knowledge this is the first time that such investigation is made. Results of the aforementioned tests prove that automatic assessment of the integrity of diagnostic images is challenging and that there are no reliable solutions currently available.