# ArtUnmasked: A Multimodal Classifier for Real, AI, and Imitated Artworks

## TL;DR

ArtUnmasked is a system that helps identify whether an artwork is real, AI-generated, or imitated, addressing challenges in digital art authenticity and copyright.

## Contribution

ArtUnmasked introduces a multimodal framework combining spectral analysis, artist filtering, and vision transformers for art classification.

## Key findings

- ArtUnmasked efficiently distinguishes AI-generated artworks from real ones using spectral artifact identification.
- The framework includes a custom dataset of 24K imitated artworks to support evaluation and future research.
- The system leverages modern vision transformers for one-shot generalization in determining authenticity or imitation.

## Abstract

Differentiating AI-generated, real, or imitated artworks is becoming a tedious and computationally challenging problem in digital art analysis. AI-generated art has become nearly indistinguishable from human-made works, posing a significant threat to copyrighted content. This content is appearing on online platforms, at exhibitions, and in commercial galleries, thereby escalating the risk of copyright infringement. This sudden increase in generative images raises concerns like authenticity, intellectual property, and the preservation of cultural heritage. Without an automated, comprehensible system to determine whether an artwork has been AI-generated, authentic (real), or imitated, artists are prone to the reduction of their unique works. Institutions also struggle to curate and safeguard authentic pieces. As the variety of generative models continues to grow, it becomes a cultural necessity to build a robust, efficient, and transparent framework for determining whether a piece of art or an artist is involved in potential copyright infringement. To address these challenges, we introduce ArtUnmasked, a practical and interpretable framework capable of (i) efficiently distinguishing AI-generated artworks from real ones using a lightweight Spectral Artifact Identification (SPAI), (ii) a TagMatch-based artist filtering module for stylistic attribution, and (iii) a DINOv3–CLIP similarity module with patch-level correspondence that leverages the one-shot generalization ability of modern vision transformers to determine whether an artwork is authentic or imitated. We also created a custom dataset of ∼24K imitated artworks to complement our evaluation and support future research. The complete implementation is available in our GitHub repository.