Enhancing Imperceptibility: Zero-width Character-based Text Steganography for Preserving Message Privacy
Bibliographic record
Abstract
Background: Text steganography preserves the privacy of secret messages by hiding them in cover text.However, existing text steganography techniques embed messages by introducing distortions in text, reducing the similarity between the cover and stegotext.Objective: The objective of this study was to design a method that increases the number of embedding choices and locations to hide more secret bits per distortion in the cover text.The goal is to enhance both embedding capacity and imperceptibility.Methods: A text steganography method is proposed that uses eight zero-width characters (ZWCs) to embed secret messages in the cover text.The proposed method also treats every character in the cover text as a potential embedding location.With eight embedding choices and bit encoding based on embedding locations, more bits can be hidden with fewer insertions in cover text.Results: Experimental results confirm that the proposed method embeds a greater number of bits per insertion of ZWC in the cover text.It also requires a smaller number of insertions to embed secret messages of comparable length.Consequently, the proposed method achieves higher embedding capacity and better imperceptibility compared to existing text steganography methods. Conclusion:The proposed method presents a substantial improvement in text steganography by increasing embedding capacity per distortion and preserving high similarity between cover and stegotext, thus enabling more secure covert communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".