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

• URL: http://arxiv.org/abs/2501.03288v1
• Date: Mon, 06 Jan 2025 06:15:10 GMT
• Title: CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models
• Authors: Zhenyu Xu, Victor S. Sheng,
• Abstract summary: The rise of large language models (LLMs) has significantly improved automated code generation, enhancing software development efficiency.<n>Existing detection methods, such as pre-trained models and watermarking, face limitations in adaptability and computational efficiency.<n>We propose a novel detection method using 2D token probability maps combined with vision models, preserving spatial code structures.
• Score: 28.711745671275477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings [32.72039589832989]
  Large language models (LLMs) have revolutionized code generation, automating programming with remarkable efficiency. These advancements challenge programming skills, ethics, and assessment integrity, making the detection of LLM-generated code essential for maintaining accountability and standards. We propose a framework capable of distinguishing between human- and LLM-written code across multiple programming languages, code generators, and domains.
  arXiv  Detail & Related papers  (2025-03-17T21:41:37Z)
• Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction [52.09472099976885]
  IAR is an Improved AutoRegressive Visual Generation Method that enhances the training efficiency and generation quality of LLM-based visual generation models. Our method consistently enhances the model training efficiency and performance from 100M to 1.4B, reducing the training time by half while achieving the same FID.
  arXiv  Detail & Related papers  (2025-01-01T15:58:51Z)
• zsLLMCode: An Effective Approach for Code Embedding via LLM with Zero-Shot Learning [6.976968804436321]
  This paper proposes a novel zero-shot approach, zsLLMCode, to generate code embeddings by using large language models (LLMs) and sentence embedding models. The results have demonstrated the effectiveness and superiority of our method over state-of-the-art unsupervised approaches.
  arXiv  Detail & Related papers  (2024-09-23T01:03:15Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs. We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models [12.959392500354223]
  We pioneer the transfer of knowledge from pre-trained code generation models to code understanding tasks. We introduce CL4D, a contrastive learning method designed to enhance the representation capabilities of decoder-only models.
  arXiv  Detail & Related papers  (2024-06-18T06:52:14Z)
• Validating LLM-Generated Programs with Metamorphic Prompt Testing [8.785973653167112]
  Large Language Models (LLMs) are increasingly integrated into the software development lifecycle. This paper proposes a novel solution called metamorphic prompt testing to address these challenges. Our evaluation on HumanEval shows that metamorphic prompt testing is able to detect 75 percent of the erroneous programs generated by GPT-4, with a false positive rate of 8.6 percent.
  arXiv  Detail & Related papers  (2024-06-11T00:40:17Z)
• M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection [52.4455893010468]
  Large Language Models (LLMs) have strong capabilities in code comprehension, but fine-tuning costs and semantic alignment issues limit their project-specific optimization. Code models such CodeBERT are easy to fine-tune, but it is often difficult to learn vulnerability semantics from complex code languages. This paper introduces the Multi-Model Collaborative Vulnerability Detection approach (M2CVD) to improve the detection accuracy of code models.
  arXiv  Detail & Related papers  (2024-06-10T00:05:49Z)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation.<n>CodeIP is a novel multi-bit watermarking technique that inserts additional information to preserve provenance details.<n>Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation [22.219645213202178]
  This paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. We show that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
  arXiv  Detail & Related papers  (2023-10-16T05:09:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.