通过局部图像块分析，精准识别人机共创画作的作者归属。
Patch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings
- 基于图像块的分析框架，结合交叉验证提升定位精度
- 局部准确率达88.8%，整体画作识别达86.7%优于基线方法
- 适用于人机协作创作场景，为版权认定提供可扩展方法
随着代理型AI在创意生产中日益深入，作者身份记录对艺术家、收藏家和法律领域愈发重要。本文提出一种基于图像块的局部作者归属分析框架，应用于一位人类艺术家与一台机器人在15幅抽象画作中的协作实践。利用普通平板扫描仪和留一画交叉验证，该方法在局部图像块级别实现88.8%的准确率（以多数投票方式在画作级别达到86.7%），显著优于基于纹理和预训练特征的基线模型（68.0%-84.7%）。对于作者身份本就模糊的协作作品，采用条件香农熵量化风格重叠程度；人工标注的混合区域不确定性比纯绘画高64%（p=0.003），表明模型能有效识别混合创作而非误判。该模型虽针对特定人机组合，但为数据稀缺的人机协同创作提供了高效溯源方法，未来可推广至任意人机协作画作的作者认定。
原文摘要 · Abstract (English)
As agentic AI becomes increasingly involved in creative production, documenting authorship has become critical for artists, collectors, and legal contexts. We present a patch-based framework for spatial authorship attribution within human-robot collaborative painting practice, demonstrated through a forensic case study of one human artist and one robotic system across 15 abstract paintings. Using commodity flatbed scanners and leave-one-painting-out cross-validation, the approach achieves 88.8% patch-level accuracy (86.7% painting-level via majority vote), outperforming texture-based and pretrained-feature baselines (68.0%-84.7%). For collaborative artworks, where ground truth is inherently ambiguous, we use conditional Shannon entropy to quantify stylistic overlap; manually annotated hybrid regions exhibit 64% higher uncertainty than pure paintings (p=0.003), suggesting the model detects mixed authorship rather than classification failure. The trained model is specific to this human-robot pair but provides a methodological grounding for sample-efficient attribution in data-scarce human-AI creative workflows that, in the future, has the potential to extend authorship attribution to any human-robot collaborative painting.
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