Efficient and Universal Watermarking for LLM-Generated Code Detection

TL;DR

This paper introduces ACW, a training-free, universal watermarking method for detecting AI-generated code by applying semantic-preserving transformations, achieving high efficiency, robustness, and broad applicability across various LLMs.

Contribution

The paper presents ACW, a novel, training-free watermarking technique for code detection that is universal, efficient, and resilient against code optimizations.

Findings

ACW effectively detects AI-generated code.

ACW preserves code utility and is resilient to optimizations.

ACW is efficient and universal across different LLMs.

Abstract

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty or the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plug-and-play watermarking approach for AI-generated code detection, named ACW (AI Code Watermarking). ACW is training-free and works by selectively applying a set of carefully-designed, semantic-preserving and idempotent code transformations to LLM code outputs. The presence or absence of the transformations serves as implicit watermarks, enabling the detection of AI-generated code. Our experimental results show that ACW effectively detects AI-generated code, preserves code utility, and is resilient against code optimizations. Especially, ACW is efficient and is universal across different LLMs, addressing the limitations of existing approaches.