Efficient Provably Secure Linguistic Steganography via Range Coding (2026.acl-long)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method outperforms existing methods in embedding capacity and embeddability. |
Similar Papers
Provably Secure Generative Linguistic Steganography (2021.findings-acl)
Copied to clipboard
| 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 . |
Breaking the "Provable Security": Detecting Finite-Precision Artifacts in LLM-based Steganography via Low-Probability Vanishing (2026.findings-acl)
Copied to clipboard
| 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. |
| Approach: | They propose a framework that transforms the detection task from semantic classification to a statistical audit of the sampling mechanism. |
| Outcome: | The proposed framework breaks the security of AC and Meteor with high detection accuracy, whereas state-of-the-art semantic steganalyzers degrade to random guessing. |
OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions (2026.eacl-long)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method should embed secret bits in as few language tokens as possible while keeping the stego-text as natural as possible. |
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding (2020.emnlp-main)
Copied to clipboard
| 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. |
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model (2021.naacl-main)
Copied to clipboard
| 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. |
| Approach: | They propose to use edit-based linguistic steganography to generate genuine-looking texts by using a masked language model that eliminates painstaking rule construction and has a high payload capacity. |
| Outcome: | The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. |
Towards Near-imperceptible Steganographic Text (P19-1)
Copied to clipboard
| 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. |
Neural Linguistic Steganography (D19-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed technique generates realistic looking cover sentences as evaluated by humans while preserving security by matching the cover message distribution with the language model distribution. |
Look Who’s Talking Now: Covert Channels From Biased LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | steganography encodes hidden messages into model-generated tokens . tradeoff between how much hidden information can be introduced and how much the model can be perturbed is important . |
| Approach: | They propose to use large language model-based steganography to encode hidden messages into model-generated tokens. |
| Outcome: | The proposed techniques are nearly optimal under a practical but difficult set of constraints . the proposed techniques ensure that only someone with the appropriate decoding key can access the hidden information . |
Zero-shot Generative Linguistic Steganography (2024.naacl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed method produces 1.926 more innocent and intelligible stegotext than any other method. |
Addressing Segmentation Ambiguity in Neural Linguistic Steganography (2022.aacl-short)
Copied to clipboard
| Challenge: | Recent studies on neural linguistic steganography ignore the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper’s suspicion. |
| Approach: | They propose to decode a secret message in a way that does not arouse suspicion of the eavesdropper. |
| Outcome: | The proposed techniques are applicable to languages without explicit word boundaries. |