Art Forgery Detection using Kolmogorov Arnold and Convolutional Neural Networks

TL;DR

This paper presents an AI-based framework for art forgery detection focusing on Wolfgang Beltracchi, comparing convolutional neural networks and Kolmogorov Arnold Networks, and demonstrating their effectiveness in identifying forgeries.

Contribution

It introduces a novel application of Kolmogorov Arnold Networks in art forgery detection and compares it with CNN-based models, specifically targeting a known forger's works.

Findings

CNN model achieves high accuracy in classifying forgeries.

KAN results align with CNN predictions on flagged artworks.

The approach effectively identifies forgeries of Wolfgang Beltracchi.

Abstract

Art authentication has historically established itself as a task requiring profound connoisseurship of one particular artist. Nevertheless, famous art forgers such as Wolfgang Beltracchi were able to deceive dozens of art experts. In recent years Artificial Intelligence algorithms have been successfully applied to various image processing tasks. In this work, we leverage the growing improvements in AI to present an art authentication framework for the identification of the forger Wolfgang Beltracchi. Differently from existing literature on AI-aided art authentication, we focus on a specialized model of a forger, rather than an artist, flipping the approach of traditional AI methods. We use a carefully compiled dataset of known artists forged by Beltracchi and a set of known works by the forger to train a multiclass image classification model based on EfficientNet. We compare the results with Kolmogorov Arnold Networks (KAN) which, to the best of our knowledge, have never been tested in the art domain. The results show a general agreement between the different models' predictions on artworks flagged as forgeries, which are then closely studied using visual analysis.