# CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models

arXiv: https://arxiv.org/abs/2501.03288 · HTML: https://mlsanity.com/paper/2501.03288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios](https://mlsanity.com/paper/2507.21693/) (2025)
- [FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals](https://mlsanity.com/paper/2605.04157/) (2026)

## Abstract (arXiv)

The rise of large language models (LLMs) like ChatGPT has significantly improved automated code generation, enhancing software development efficiency. However, this introduces challenges in academia, particularly in distinguishing between human-written and LLM-generated code, which complicates issues of academic integrity. Existing detection methods, such as pre-trained models and watermarking, face limitations in adaptability and computational efficiency. In this paper, we propose a novel detection method using 2D token probability maps combined with vision models, preserving spatial code structures such as indentation and brackets. By transforming code into log probability matrices and applying vision models like Vision Transformers (ViT) and ResNet, we capture both content and structure for more accurate detection. Our method shows robustness across multiple programming languages and improves upon traditional detectors, offering a scalable and computationally efficient solution for identifying LLM-generated code.