Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication

TL;DR

This paper introduces an AI-based method for analyzing strokes in line drawings to attribute authorship and detect forgeries, achieving high accuracy and robustness in distinguishing genuine artworks from fakes.

Contribution

It presents a novel stroke segmentation algorithm, compares various features and classification methods, and demonstrates effective attribution and authentication on a diverse dataset.

Findings

Stroke classification accuracy of 70%-90%.

Drawing-level attribution accuracy above 80%.

100% fake detection accuracy in most cases.

Abstract

This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We designed and compared different hand-crafted and learned features for the task of quantifying stroke characteristics. We also propose and compare different classification methods at the drawing level. We experimented with a dataset of 300 digitized drawings with over 80 thousands strokes. The collection mainly consisted of drawings of Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of representative works of other artists. The experiments shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with accuracy above 80%, while being robust to be deceived by fakes (with accuracy 100% for detecting fakes in most settings).