A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix
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
In this paper, a coverless information hiding algorithm is introduced. In which, the grayscale gradient co-occurrence matrix is used to encode images and the mapping relationship between the images and the random numbers is used to express the payload information. There are three steps for this algorithm. Firstly, the grayscale gradient co-occurrence matrix of cover image is calculated, in accordance with which a descriptor is introduced. Secondly, the descriptor is quantized into a binary sequence to construct a mapping relationship between the cover image and the binary random numbers. Finally, the binary secret information sequence is divided into many segments, and the correct images corresponding to those segments are selected from the image database according to the mapping relationship. Moreover, the secret information is encrypted by Turbo encoder to improve the security. The experimental results show that the proposed algorithm has a good tolerance towards JPEG compression attack and low-pass filter attack. This promising algorithm which can be applied into remote sensing satellites leads an applied value in covert communication with high-security.
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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 | 0.001 |
| 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 it