# Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features

arXiv: https://arxiv.org/abs/2502.17749 · HTML: https://mlsanity.com/paper/2502.17749/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://mlsanity.com/paper/2303.17568/) (2023)
- [Who Wrote this Code? Watermarking for Code Generation](https://mlsanity.com/paper/2305.15060/) (2023)
- [Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion](https://mlsanity.com/paper/2405.19782/) (2024)
- [Language Models for Code Completion: A Practical Evaluation](https://mlsanity.com/paper/2402.16197/) (2024)
- [How Large Language Models are Transforming Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2210.03568/) (2022)
- [Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers](https://mlsanity.com/paper/2401.06461/) (2024)
- [Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance](https://mlsanity.com/paper/2404.08817/) (2024)
- [Zero-Shot Detection of Machine-Generated Codes](https://mlsanity.com/paper/2310.05103/) (2023)
- [A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts](https://mlsanity.com/paper/2311.08374/) (2023)
- [InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation](https://mlsanity.com/paper/2411.01063/) (2024)
- [Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations](https://mlsanity.com/paper/2111.10793/) (2021)
- [Evaluating AIGC Detectors on Code Content](https://mlsanity.com/paper/2304.05193/) (2023)
- [GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench](https://mlsanity.com/paper/2308.13963/) (2023)
- [ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation](https://mlsanity.com/paper/2501.18460/) (2025)
- [Is this Snippet Written by ChatGPT? An Empirical Study with a CodeBERT-Based Classifier](https://mlsanity.com/paper/2307.09381/) (2023)
- [CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection](https://mlsanity.com/paper/2405.00428/) (2024)

## Built on by (most cited first, 4 of 4)

- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [AICD Bench: A Challenging Benchmark for AI-Generated Code Detection](https://mlsanity.com/paper/2602.02079/) (2026)
- [code_transformed: The Influence of Large Language Models on Code](https://mlsanity.com/paper/2506.12014/) (2025)
- [Bin2Vec: Interpretable and Auditable Multi-View Binary Analysis for Code Plagiarism Detection](https://mlsanity.com/paper/2512.02197/) (2025)

## Abstract (arXiv)

Recent progress in large language models (LLMs) for code generation has raised serious concerns about intellectual property protection. Malicious users can exploit LLMs to produce paraphrased versions of proprietary code that closely resemble the original. While the potential for LLM-assisted code paraphrasing continues to grow, research on detecting it remains limited, underscoring an urgent need for detection system. We respond to this need by proposing two tasks. The first task is to detect whether code generated by an LLM is a paraphrased version of original human-written code. The second task is to identify which LLM is used to paraphrase the original code. For these tasks, we construct a dataset LPcode consisting of pairs of human-written code and LLM-paraphrased code using various LLMs. We statistically confirm significant differences in the coding styles of human-written and LLM-paraphrased code, particularly in terms of naming consistency, code structure, and readability. Based on these findings, we develop LPcodedec, a detection method that identifies paraphrase relationships between human-written and LLM-generated code, and discover which LLM is used for the paraphrasing. LPcodedec outperforms the best baselines in two tasks, improving F1 scores by 2.64% and 15.17% while achieving speedups of 1,343x and 213x, respectively. Our code and data are available at https://github.com/Shinwoo-Park/detecting_llm_paraphrased_code_via_coding_style_features.