Title: Analysis, attribution, and authentication of drawings with convolutional neural networks

Authors: Steven J. Frank; Andrea M. Frank

Addresses: Art Eye-D Associates LLC, 779 Salem End Road, Framingham, MA 01702, USA ' Art Eye-D Associates LLC, 779 Salem End Road, Framingham, MA 01702, USA

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.

Keywords: convolutional neural network; attribution; authentication; drawings.

DOI: 10.1504/IJART.2022.10050958

International Journal of Arts and Technology, 2022 Vol.14 No.3, pp.192 - 205

Received: 18 Dec 2021
Accepted: 08 Aug 2022
Published online: 23 Jan 2023 *

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