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.

本研究解决了在软件开发中识别人类编写代码与大语言模型（LLM）生成代码之间的难题，尤其是对学术诚信造成的挑战。我们提出了一种新颖的检测方法，通过结合二维标记概率图与视觉模型，能够保留代码的空间结构，显著提高了对多种编程语言中LLM生成代码的检测准确性与效率。

CodeVision：使用二维标记概率图和视觉模型检测大语言模型生成的代码