用AI生成仿画检测真迹，识别准确率97.2%却难找相似图
Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval
- 用DINOv2特征匹配，不训练直接比相似度
- 识别准确率达97.2%，但检索平均精度仅29.0%
- 微调后检索提升12% AP，但识别率降为92.7%
艺术抄袭检测在保护艺术家版权方面至关重要，但仍是法证分析中的难题。本文针对抄袭绘画的识别与解释，提出通过检索视觉相似的真实作品来实现。为此，我们构建了一个数据集，收集绘画照片并使用生成式AI合成特定艺术家风格的仿作。首先采用现成的DINOv2视觉基础模型特征作为基线，通过相似度阈值分类抄袭。令人意外的是，该非学习方法达到97.2%的识别准确率，但检索平均精度（AP）仅为29.0%。为进一步提升检索质量，我们在数据库中采样正负样本对，使用度量学习损失微调DINOv2。微调后检索性能提升12% AP，但识别准确率下降至92.7%。论文最后进行深入讨论，并提出未来研究方向。
原文摘要 · Abstract (English)
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.
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