# Innamark: A Whitespace Replacement Information-Hiding Method

arXiv: https://arxiv.org/abs/2502.12710 · HTML: https://mlsanity.com/paper/2502.12710/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 5 of its 64 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 5 of 5)

- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [A survey of deep neural network watermarking techniques](https://mlsanity.com/paper/2103.09274/) (2021)
- [Pseudorandom Error-Correcting Codes](https://mlsanity.com/paper/2402.09370/) (2024)
- [Benchmarking as Empirical Standard in Software Engineering Research](https://mlsanity.com/paper/2105.00272/) (2021)

## Built on by (most cited first, 3 of 3)

- [Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique](https://mlsanity.com/paper/2510.09655/) (2025)
- [LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization](https://mlsanity.com/paper/2607.10103/) (2026)
- [Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models](https://mlsanity.com/paper/2512.13325/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have gained significant popularity in recent years. Differentiating between a text written by a human and one generated by an LLM has become almost impossible. Information-hiding techniques such as digital watermarking or steganography can help by embedding information inside text in a form that is unlikely to be noticed. However, existing techniques, such as linguistic-based or format-based methods, change the semantics or cannot be applied to pure, unformatted text. In this paper, we introduce a novel method for information hiding called Innamark, which can conceal any byte-encoded sequence within a sufficiently long cover text. This method is implemented as a multi-platform library using the Kotlin programming language, which is accompanied by a command-line tool and a web interface. By substituting conventional whitespace characters with visually similar Unicode whitespace characters, our proposed scheme preserves the semantics of the cover text without changing the number of characters. Furthermore, we propose a specified structure for secret messages that enables configurable compression, encryption, hashing, and error correction. An experimental benchmark comparison on a dataset of 1 000 000 Wikipedia articles compares ten algorithms. The results demonstrate the robustness of our proposed Innamark method in various applications and the imperceptibility of its watermarks to humans. We discuss the limits to the embedding capacity and robustness of the algorithm and how these could be addressed in future work.