Using Deep Learning Techniques for Authentication of Amadeo de Souza Cardoso Paintings and Drawings

Abstract

This paper investigates the application of a Convolutional Neural Network (CNN), AlexNet, on the authentication of paintings by different artists, including Portuguese painter Amadeo de Souza Cardoso, Chinese painter Daqian Zhang and Dutch painter Vincent van Gogh. The research is motivated by the studies on the identification of the works by Amadeo based on the painter’s brushstroke implementing Machine Learning algorithms combined with material analysis. The employment of CNN intends to improve the performance of the brushstroke analysis and increase the accuracy while authenticating an artist’ works. The results show that the implementation of AlexNet produces higher accuracies than its counterparts applying previous brushstroke analysis. Notably, when Amadeo drawings are included in the testing based on Amadeo paintings, the accuracies obtained with the original algorithm drop substantially, whilst the counterparts attained with AlexNet improved considerably. However, when other testing sets are introduced, especially the Chinese paintings, the accuracies show a great increase with the original algorithm but a significant decrease with AlexNet. It implies that AlexNet surpasses the traditional computation through learning by examples; it can potentially solve the problem of limited number of artworks by a specific artist for training.