发表机构
UNSW Sydney（新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个生产中心框架，比较直接生成与代码渲染两条视觉创作路径的检测和水印，并给出十个研究问题，形成概念性研究议程。
AI 中文摘要
AI系统通过图像/视频生成模型或编写代码和图形描述后进行渲染来创建图像和视频。这些途径能产生相似的可见产物，但暴露了不同的表示、干预点和溯源证据。我们开发了一个以生产为中心的框架，比较了两种途径下的检测和水印。一个明确的验证规范区分了被动推理、消息恢复和认证溯源。我们按生产阶段组织了图像、视频、源代码和渲染感知水印。我们考察了生成图像和视频、绘图和SVG、可编程视频以及智能体组合工作流的不同需求。已记录的Claude、OpenAI和渲染工具接口将框架连接到具体系统。我们提出了十个范围明确的研究问题，涉及可识别性、可观测性、跨阶段的公平比较、可恢复载荷、重建、同步、组合、混合局部贡献和私有生产事件认证。结果是一个基于已发表方法、检查过的接口和基本边界示例的概念性研究议程。它报告了无实验，并声称无新定理；其附录结果是基本计算，文档和源代码检查建立了接口，而非经验鲁棒性。
英文摘要
AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These routes can produce similar visible artifacts but expose different representations, intervention points, and provenance evidence. We develop a production-centered framework that compares detection and watermarking across both routes. An explicit verification specification distinguishes passive inference, message recovery, and authenticated provenance. We organize image, video, source-code, and rendering-aware watermarks by production stage. We examine the different requirements of generated images and video, plots and SVG, programmable video, and agent-composed workflows. Documented Claude, OpenAI, and rendering-tool interfaces connect the framework to concrete systems. We pose ten scoped research questions on identifiability, observability, fair comparison across stages, recoverable payload, reconstruction, synchronization, composition, hybrid local contribution, and private production-event authentication. The result is a conceptual research agenda grounded in published methods, inspected interfaces, and elementary boundary examples. It reports no experiments and claims no new theorems; its appendix results are elementary calculations, and documentation and source inspection establish interfaces, not empirical robustness.
Comments46 pages, 6 figures, 4 tables. Conceptual research agenda; no experiments. Video: https://youtu.be/14SMl0d_e48. Project page: https://zhenggao-30.github.io/Rethinking-Visual-Provenance/
