# DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity

arXiv: https://arxiv.org/abs/2601.12505 · HTML: https://mlsanity.com/paper/2601.12505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 14 of 14)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Technical Report on the Pangram AI-Generated Text Classifier](https://mlsanity.com/paper/2402.14873/) (2024)
- [Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models](https://mlsanity.com/paper/2505.16957/) (2025)
- [TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents](https://mlsanity.com/paper/2506.00089/) (2025)
- [Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation](https://mlsanity.com/paper/2410.09318/) (2024)
- [Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform](https://mlsanity.com/paper/2101.12344/) (2021)

## Built on by (most cited first, 1 of 1)

- [Show, Don't TELL: Explainable AI-Generated Text Detection](https://mlsanity.com/paper/2605.27921/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) can directly consume exam documents, threatening conventional assessments and academic integrity. We present DoPE (Decoy-Oriented Perturbation Encapsulation), a document-layer defense framework that embeds semantic decoys into PDF/HTML assessments to exploit render-parse discrepancies in MLLM pipelines. By instrumenting exams at authoring time, DoPE provides model-agnostic prevention (stop or confound automated solving) and detection (flag blind AI reliance) without relying on conventional one-shot classifiers. We formalize prevention and detection tasks, and introduce FewSoRT-Q, an LLM-guided pipeline that generates question-level semantic decoys and FewSoRT-D to encapsulate them into watermarked documents. We evaluate on Integrity-Bench, a novel benchmark of 1826 exams (PDF+HTML) derived from public QA datasets and OpenCourseWare. Against black-box MLLMs from OpenAI and Anthropic, DoPE yields strong empirical gains: a 91.4% detection rate at an 8.7% false-positive rate using an LLM-as-Judge verifier, and prevents successful completion or induces decoy-aligned failures in 96.3% of attempts. We release Integrity-Bench, our toolkit, and evaluation code to enable reproducible study of document-layer defenses for academic integrity.