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.

翻译：随着具身化人工智能日益深入创意生产过程，记录作者身份对于艺术家、收藏者和法律场景变得至关重要。本文提出一种基于图像块的框架，用于人机协作绘画实践中的空间作者归属，并通过一项包含一位人类艺术家与一个机器人系统完成的15幅抽象绘画的法证案例研究进行验证。该方法使用商用平板扫描仪和留一绘画交叉验证，实现了88.8%的图像块级别准确率（通过多数投票达到86.7%的绘画级别准确率），优于基于纹理和预训练特征的基线方法（68.0%-84.7%）。针对真实作者身份存在固有模糊性的协作艺术作品，我们采用条件香农熵量化风格重叠度；人工标注的混合区域显示出比纯作者区域高64%的不确定性（p=0.003），表明模型检测到的是混合作者身份而非分类失败。训练所得模型虽针对特定人机组合，但为数据稀缺的人机创意工作流中实现样本高效的作者归属提供了方法论基础，未来有望将作者归属能力扩展至任意人机协作绘画场景。