| Challenge: | Existing methods of linguistic steganography generate high-security stegotext with statistical differences between the conditional probability distributions of stegot and natural text, which brings about security risks. |
| Approach: | They propose a method which embeds secret information by Adaptive Dynamic Grouping of tokens according to their probability given by an off-the-shelf language model. |
| Outcome: | The proposed method generates steganographic text with perfect security . it is based on three public corpora and proves its security based upon mathematical tests . |
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| Challenge: | Linguistic steganography is a promising field in safeguarding information . previous methods have achieved perfect imperceptibility but at the expense of embedding capacity. |
| Approach: | They propose to use a classical entropy coding method to achieve secure steganography . they propose to employ a rotation mechanism to achieve embedding efficiency . |
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Zero-shot Generative Linguistic Steganography (2024.naacl-long)
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| Challenge: | Generative linguistic steganography attempts to hide secret messages into covertext . previous studies focused on the statistical differences between the covertext and stegotext - however, ill-formed stegotas can readily be identified by humans . |
| Approach: | They propose a zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. |
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OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions (2026.eacl-long)
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| Challenge: | In coverless steganography, secret bits are embedded in as few language tokens as possible . stego-texts can be decoded by eavesdroppers, but are difficult to detect . |
| Approach: | They propose a method to embed secret bits in language tokens using a Large Language Model . they propose maximizing the entropy of a replacement probability distribution . |
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Neural Linguistic Steganography (D19-1)
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| Challenge: | linguistic steganography encrypts a secret message into a cover signal . language is a pragmatic cover signal due to its benign occurrence and independence from any one medium. |
| Approach: | They propose a technique that encrypts a secret message into a cover signal . language is a particularly pragmatic cover signal due to its benign occurrence . |
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Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model (2021.naacl-main)
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| Challenge: | linguistic steganography is the practice of concealing a secret message in some cover data such that an eavesdropper is not even aware of the existence of the secret message. |
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Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding (2020.emnlp-main)
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| Challenge: | Linguistic steganography studies how to hide secret messages in natural language cover texts. |
| Approach: | They propose a method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits/word and KL metrics. |
Breaking the "Provable Security": Detecting Finite-Precision Artifacts in LLM-based Steganography via Low-Probability Vanishing (2026.findings-acl)
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| Challenge: | Recent advances in Large Language Models have fostered a new class of generative linguistic steganography, claim “provably secure” by theoretically aligning the stego distribution with the language model’s natural distribution. |
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Towards Near-imperceptible Steganographic Text (P19-1)
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| Challenge: | Existing methods for linguistic steganography are vulnerable to automated detection. |
| Approach: | They propose an encoding algorithm with improved near-imperceptible guarantees based on implicit assumptions on statistical behaviors of fluent text. |
| Outcome: | The proposed algorithm improves on existing steganographic systems with near-imperceptible guarantees. |
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking Agent (2025.emnlp-main)
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| Challenge: | Existing work has focused on the (un)intended leakage of sensitive information through LLM outputs. |
| Approach: | They propose a threat model that embeds context information into natural-looking outputs via linguistic steganography without requiring explicit control over inference inputs. |
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Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography (2026.acl-long)
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| Challenge: | linguistic steganography assumes that stegographic texts are fragile to even minor modifications, compromising text quality. |
| Approach: | They propose an anchored sliding window framework to improve imperceptibility and robustness . they propose to include the prompt and a bridge context within the context window . |
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