Analysis, attribution, and authentication of drawings with convolutional neural networks Online publication date: Mon, 23-Jan-2023
by Steven J. Frank; Andrea M. Frank
International Journal of Arts and Technology (IJART), Vol. 14, No. 3, 2022
Abstract: We propose an innovative framework for assessing the probability that a subject drawing is the work of a particular artist. While numerous efforts have applied neural networks to classify two-dimensional works of art by style and author, these efforts – with few exceptions – have been limited to paintings. Drawings, which can involve multiple media with very different visual characteristics and greater susceptibility to damage than paint, present a more formidable challenge. Our technique is robust to the age and wear of a drawing as well as the possibility that it contains marks made with multiple drawing media. We obtained classification accuracies exceeding 90% using a five-layer convolutional neural network (CNN), which we trained on a curated set of drawing images attributed to Raffaello Sanzio da Urbino (1483-1520), known as Raphael, as well as drawings by his admirers, imitators, and forgers.
Online publication date: Mon, 23-Jan-2023
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