Towards Code Watermarking with Dual-Channel Transformations

TL;DR

This paper introduces SrcMarker, a novel source code watermarking system that uses dual-channel transformations and learning-based techniques to embed ownership identifiers into code without affecting its functionality or readability.

Contribution

The paper presents a new watermarking approach that combines AST-based transformations with learning modules, enabling language-agnostic, unobtrusive, and end-to-end trainable source code watermarking.

Findings

SrcMarker outperforms existing watermarking methods in various metrics.

The system effectively preserves code semantics and readability.

It demonstrates robustness across multiple programming languages.

Abstract

The expansion of the open source community and the rise of large language models have raised ethical and security concerns on the distribution of source code, such as misconduct on copyrighted code, distributions without proper licenses, or misuse of the code for malicious purposes. Hence it is important to track the ownership of source code, in which watermarking is a major technique. Yet, drastically different from natural languages, source code watermarking requires far stricter and more complicated rules to ensure the readability as well as the functionality of the source code. Hence we introduce SrcMarker, a watermarking system to unobtrusively encode ID bitstrings into source code, without affecting the usage and semantics of the code. To this end, SrcMarker performs transformations on an AST-based intermediate representation that enables unified transformations across different programming languages. The core of the system utilizes learning-based embedding and extraction modules to select rule-based transformations for watermarking. In addition, a novel feature-approximation technique is designed to tackle the inherent non-differentiability of rule selection, thus seamlessly integrating the rule-based transformations and learning-based networks into an interconnected system to enable end-to-end training. Extensive experiments demonstrate the superiority of SrcMarker over existing methods in various watermarking requirements.