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

arXiv: https://arxiv.org/abs/1711.03536 · HTML: https://mlsanity.com/paper/1711.03536/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning](https://mlsanity.com/paper/2103.01359/) (2021)
- [The Work of Art in an Age of Mechanical Generation](https://mlsanity.com/paper/2101.11587/) (2021)
- [Patch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings](https://mlsanity.com/paper/2602.17030/) (2026)
- [IDraw: Artist Verification from Digital Drawing Images](https://mlsanity.com/paper/2608.01737/) (2026)

## Abstract (arXiv)

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).