A Linguistic Steganalysis Approach Base on Source Features of Text and Immune Mechanism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Linguistic steganalysis is a technique that discovering potentially hidden information embedded through using linguistically in plain text using. Varieties of syntax and multi-meanings of semantics for linguistics augment the difficulty of linguistic steganalysis intensely, thereby it is a challenge area. In this paper, we propose a novel steganalysis method for linguistics based on immune. This method has two attributions: i). basis statistical features of text are employed for blind steganalysis ii). immune technique is chosen to build a two-level detection mechanism to detect two categories of stego text respectively, one of which is Success-Stego-text and another is False-Stego-text. Appropriate detections are generated and preferable features are signed. Experiments prove the approach has higher accuracy than current steganalysis algorithms. Especially when the segment size of text is greater than 3kB, the accuracies of detecting for natural text and stego text are both more than 95%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it