Abstract
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.
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Publication details
- Journal
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- Open access
- Green open access
Cite this article
APA 7
Gao, Z., Li, X., Bao, Z., Song, Y., & Jiang, J. (2026). Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering. https://omanscience.com/en/articles/rethinking-visual-provenance-detection-and-watermarking-across-direct-visual-generation-and-llm-driven-code-rendering
MLA 9
Gao, Zheng, et al. "Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering." https://omanscience.com/en/articles/rethinking-visual-provenance-detection-and-watermarking-across-direct-visual-generation-and-llm-driven-code-rendering.
Chicago (author–date)
Gao, Zheng, Xiaoyu Li, Zhicheng Bao, Yang Song, and Jiaojiao Jiang. 2026. "Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering." https://omanscience.com/en/articles/rethinking-visual-provenance-detection-and-watermarking-across-direct-visual-generation-and-llm-driven-code-rendering.
Harvard
Gao, Z., Li, X., Bao, Z., Song, Y. and Jiang, J. (2026) 'Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering', Available at: https://omanscience.com/en/articles/rethinking-visual-provenance-detection-and-watermarking-across-direct-visual-generation-and-llm-driven-code-rendering.
Vancouver
Gao Z, Li X, Bao Z, Song Y, Jiang J. Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering. https://omanscience.com/en/articles/rethinking-visual-provenance-detection-and-watermarking-across-direct-visual-generation-and-llm-driven-code-rendering
IEEE
Z. Gao, X. Li, Z. Bao, Y. Song, and J. Jiang, "Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering," https://omanscience.com/en/articles/rethinking-visual-provenance-detection-and-watermarking-across-direct-visual-generation-and-llm-driven-code-rendering.