02000nas a2200241   4500000000100000000000100001008004100002260001200043653002800055653001900083653002100102653002100123653001900144100002800163700002800191700001800219245015200237856007900389300000900468490000600477520126100483022001401744       2022                            d  c12/202210aArtificial Intelligence10aAuthentication10aImage Generation10aMachine Learning10aNeural Network1 aMarcelo Fraile-Narváez1 aIsmael Sagredo-Olivenza1 aNadia McGowan00aPainting Authorship and Forgery Detection Challenges with AI Image Generation Algorithms: Rembrandt and 17th Century Dutch Painters as a Case Study  uhttps://www.ijimai.org/journal/sites/default/files/2022-11/ijimai7_7_1.pdf  a7-130 v73 aImage authorship attribution presents many challenges and difficulties which have increased with the capabilities presented by synthetic image generation through different artificial intelligence algorithms available today. The hypothesis in this research considers the possibility of using artificial intelligence as a tool to detect forgeries through the usage of a deep learning algorithm. The proposed algorithm was trained using a dataset comprised of paintings by Rembrandt and other 17th century Dutch painters. Three experiments were performed with the proposed algorithm. The first was to build a classifier able to ascertain whether a painting belongs to the Rembrandt or non-Rembrandt category, depending on whether it was painted by this author or not. The second tests included other 17th century painters in four categories. Artworks could be classified as Rembrandt, Eeckhout, Leveck or other Dutch painters. The third experiment used paintings generated by Dall-e 2 and attempted to classify them using the prior categories. Experiments confirmed the hypothesis with best executions reaching accuracy rates of more than 90%. Future research with extended datasets and improved image resolution are suggested to improve the obtained results.  a1989-1660