# Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography

arXiv: https://arxiv.org/abs/2604.09066 · HTML: https://mlsanity.com/paper/2604.09066/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [Non-Determinism of "Deterministic" LLM Settings](https://mlsanity.com/paper/2408.04667/) (2024)
- [Coercing LLMs to do and reveal (almost) anything](https://mlsanity.com/paper/2402.14020/) (2024)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)
- [Generating Steganographic Text with LSTMs](https://mlsanity.com/paper/1705.10742/) (2017)
- [Perfectly Secure Steganography Using Minimum Entropy Coupling](https://mlsanity.com/paper/2210.14889/) (2022)
- [Provably Secure Generative Linguistic Steganography](https://mlsanity.com/paper/2106.02011/) (2021)
- [Towards Near-imperceptible Steganographic Text](https://mlsanity.com/paper/1907.06679/) (2019)
- [LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning](https://mlsanity.com/paper/2004.12817/) (2020)
- [Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding](https://mlsanity.com/paper/2010.00677/) (2020)
- [SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling](https://mlsanity.com/paper/2503.19499/) (2025)
- [Addressing Segmentation Ambiguity in Neural Linguistic Steganography](https://mlsanity.com/paper/2211.06662/) (2022)
- [Provably Robust and Secure Steganography in Asymmetric Resource Scenario](https://mlsanity.com/paper/2407.13499/) (2024)

## Built on by (most cited first, 2 of 2)

- [HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Framework](https://mlsanity.com/paper/2607.23597/) (2026)
- [A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges](https://mlsanity.com/paper/2608.29077/) (2026)

## Abstract (arXiv)

Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications. While previous work mitigates this fragility by limiting the context window, it significantly compromises text quality. In this paper, we propose the anchored sliding window (ASW) framework to improve imperceptibility and robustness. In addition to the latest tokens, the prompt and a bridge context are anchored within the context window, encouraging the model to compensate for the excluded tokens. We formulate the optimization of the bridge context as a variant of prompt distillation, which we further extend using self-distillation strategies. Experiments show that our ASW significantly and consistently outperforms the baseline method in text quality, imperceptibility, and robustness across diverse settings. The code is available at github.com/ryehr/ASW_steganography.