Frequency Domain Steganography with Reversible Texture Combination
Bibliographic record
Abstract
Texture Combination is a process of re-sampling a smaller texture image to synthesize a new texture with similar appearance. This texture combination is weaved with Steganography to conceal secret text messages. In this paper, a novel texture combination based Steganographic method in frequency domain is proposed to hide and send secret messages. In contrast to the existing techniques, this method generates a Stego synthetic texture of arbitrary size rather than embedding in the original image. This method offers an embedding capacity that is proportional to the size of the Stego synthetic texture. Moreover its reversible capability allows recovering secret messages and the source texture. The texture combination is performed in frequency domain making use of Discrete Cosine Transform (DCT) which makes it almost impossible for a Steganalytic algorithm to defeat this approach. Experimental results verify that the proposed method provides various embedding capacities, produces visually good texture images and recover secret messages.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".