# Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering

arXiv: https://arxiv.org/abs/2610.08137 · HTML: https://mlsanity.com/paper/2610.08137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 42 of its 80 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 30 of 42)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [StegaStamp: Invisible Hyperlinks in Physical Photographs](https://mlsanity.com/paper/1904.05343/) (2019)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models](https://mlsanity.com/paper/2404.04956/) (2024)
- [Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models](https://mlsanity.com/paper/2311.04378/) (2023)
- [Watermark Anything with Localized Messages](https://mlsanity.com/paper/2411.07231/) (2024)
- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [Video Seal: Open and Efficient Video Watermarking](https://mlsanity.com/paper/2412.09492/) (2024)
- [DVMark: A Deep Multiscale Framework for Video Watermarking](https://mlsanity.com/paper/2104.12734/) (2021)
- [CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields](https://mlsanity.com/paper/2307.11526/) (2023)
- [CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models](https://mlsanity.com/paper/2308.14401/) (2023)
- [VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking](https://mlsanity.com/paper/2501.14195/) (2025)
- [WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights](https://mlsanity.com/paper/2405.02066/) (2024)
- [GenVidBench: A 6-Million Benchmark for AI-Generated Video Detection](https://mlsanity.com/paper/2501.11340/) (2025)
- [GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting](https://mlsanity.com/paper/2410.23718/) (2024)
- [3D-GSW: 3D Gaussian Splatting for Robust Watermarking](https://mlsanity.com/paper/2409.13222/) (2024)
- [VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models](https://mlsanity.com/paper/2504.16359/) (2025)
- [Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking](https://mlsanity.com/paper/2505.12667/) (2025)
- [LVMark: Robust Watermark for Latent Video Diffusion Models](https://mlsanity.com/paper/2412.09122/) (2024)
- [ItoV: Efficiently Adapting Deep Learning-based Image Watermarking to Video Watermarking](https://mlsanity.com/paper/2305.02781/) (2023)
- [Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection](https://mlsanity.com/paper/2602.21593/) (2026)
- [SLICE: Semantic Latent Injection via Compartmentalized Embedding for Image Watermarking](https://mlsanity.com/paper/2603.12749/) (2026)
- [Efficient and Universal Watermarking for LLM-Generated Code Detection](https://mlsanity.com/paper/2402.07518/) (2024)
- [VideoMarkBench: Benchmarking Robustness of Video Watermarking](https://mlsanity.com/paper/2505.21620/) (2025)

## Abstract (arXiv)

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.