# Towards Code Watermarking with Dual-Channel Transformations

arXiv: https://arxiv.org/abs/2309.00860 · HTML: https://mlsanity.com/paper/2309.00860/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2023.

## Built on by (most cited first, 9 of 9)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Machine Unlearning of Pre-trained Large Language Models](https://mlsanity.com/paper/2402.15159/) (2024)
- [Towards Optimal Statistical Watermarking](https://mlsanity.com/paper/2312.07930/) (2023)
- [CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code](https://mlsanity.com/paper/2404.15639/) (2024)
- [From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models](https://mlsanity.com/paper/2406.11106/) (2024)
- [Proactive Schemes: A Survey of Adversarial Attacks for Social Good](https://mlsanity.com/paper/2409.16491/) (2024)
- [Watermarking Large Language Models and the Generated Content: Opportunities and Challenges](https://mlsanity.com/paper/2410.19096/) (2024)
- [Optimizing Token Choice for Code Watermarking: An RL Approach](https://mlsanity.com/paper/2508.11925/) (2025)
- [SWaRL: Safeguard Code Watermarking via Reinforcement Learning](https://mlsanity.com/paper/2601.02602/) (2026)

## Abstract (arXiv)

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.