Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval
Sophie Zhou, Shu Kong

TL;DR
This paper investigates the recognition and retrieval of plagiarized paintings using AI, constructing a dataset with AI-synthesized plagiarized images, and demonstrates that fine-tuning models improves retrieval but slightly reduces recognition accuracy.
Contribution
It introduces a new dataset of plagiarized paintings created with generative AI and evaluates baseline and fine-tuned models for plagiarism detection and retrieval.
Findings
Baseline DINOv2 achieves 97.2% recognition accuracy.
Fine-tuning improves retrieval precision by 12%.
Recognition accuracy slightly decreases after fine-tuning.
Abstract
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…
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Taxonomy
TopicsAesthetic Perception and Analysis · Generative Adversarial Networks and Image Synthesis · Art History and Market Analysis
