# Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments

arXiv: https://arxiv.org/abs/2601.11093 · HTML: https://mlsanity.com/paper/2601.11093/

## 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 7 of its 16 references that are in the map.
- First posted on arXiv 16 Jan 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, 7 of 7)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [ChatGPT: The End of Online Exam Integrity?](https://mlsanity.com/paper/2212.09292/) (2022)
- [Technical Report on the Pangram AI-Generated Text Classifier](https://mlsanity.com/paper/2402.14873/) (2024)
- [EditLens: Quantifying the Extent of AI Editing in Text](https://mlsanity.com/paper/2510.03154/) (2025)
- [Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs](https://mlsanity.com/paper/2406.15335/) (2024)
- [TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents](https://mlsanity.com/paper/2506.00089/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and credentials. Existing watermarking techniques either operate at the token level or assume control over the model's decoding process, making them ineffective when students query proprietary black-box systems with instructor-provided documents. We present Integrity Shield, a document-layer watermarking system that embeds schema-aware, item-level watermarks into assessment PDFs while keeping their human-visible appearance unchanged. These watermarks consistently prevent MLLMs from answering shielded exam PDFs and encode stable, item-level signatures that can be reliably recovered from model or student responses. Across 30 exams spanning STEM, humanities, and medical reasoning, Integrity Shield achieves exceptionally high prevention (91-94% exam-level blocking) and strong detection reliability (89-93% signature retrieval) across four commercial MLLMs. Our demo showcases an interactive interface where instructors upload an exam, preview watermark behavior, and inspect pre/post AI performance & authorship evidence.