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

TL;DR

This paper introduces a method to detect LLM-paraphrased code and identify the specific LLM used, leveraging coding style features, with significant improvements over baselines in accuracy and speed.

Contribution

The authors create a dataset and develop LPcodedec, a novel detection approach that outperforms existing methods in identifying paraphrased code and its originating LLM.

Findings

Significant stylistic differences between human and LLM-generated code.

LPcodedec achieves higher F1 scores than baselines.

The method is significantly faster, with over 200x speedups.

Abstract

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.