# Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval

arXiv: https://arxiv.org/abs/2506.23132 · HTML: https://mlsanity.com/paper/2506.23132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://mlsanity.com/paper/1608.01807/) (2016)
- [Learning Super-Features for Image Retrieval](https://mlsanity.com/paper/2201.13182/) (2022)
- [Solving Instance Detection from an Open-World Perspective](https://mlsanity.com/paper/2503.00359/) (2025)
- [CriSp: Leveraging Tread Depth Maps for Enhanced Crime-Scene Shoeprint Matching](https://mlsanity.com/paper/2404.16972/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generating a Paracosm for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2602.00813/) (2026)

## Abstract (arXiv)

Art plagiarism detection plays a crucial role in protecting artists' copyrights and intellectual property, yet it remains a challenging problem in forensic analysis. In this paper, we address the task of recognizing plagiarized paintings and explaining the detected plagarisms by retrieving visually similar authentic artworks. To support this study, we construct a dataset by collecting painting photos and synthesizing plagiarized versions using generative AI, tailored to specific artists' styles. We first establish a baseline approach using off-the-shelf features from the visual foundation model DINOv2 to retrieve the most similar images in the database and classify plagiarism based on a similarity threshold. Surprisingly, this non-learned method achieves a high recognition accuracy of 97.2% but suffers from low retrieval precision 29.0% average precision (AP). To improve retrieval quality, we finetune DINOv2 with a metric learning loss using positive and negative sample pairs sampled in the database. The finetuned model greatly improves retrieval performance by 12% AP over the baseline, though it unexpectedly results in a lower recognition accuracy (92.7%). We conclude with insightful discussions and outline directions for future research.