Designing a Model for Hiding Images in RGB Cover Image Based Scrambling and Encryption Methods
Why this work is in the frame
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Bibliographic record
Abstract
In the digital world, one of the crucial issues is protecting information transmitted over a public network; therefore, encryption and steganography methods must be used to raise the level of data security.This paper invests scrambling and encryption techniques to protect data and compress it to reduce its size, thus increasing system performance.The system is built on protecting gray images after passing a set of steps.The first step denotes the scrambling stage that scatters the locations of gray images by adopting a logistic map method to make it difficult for intruders.The second step contains scattering the image again using the same method but with a different equation and then performing encryption based on the xor operation.The third step represents embedding the data and includes dividing the RGB cover image into three bands where each band is divided into (4×4) blocks, and the bits are stored in the location (2,2) from each band.It tested the system's efficiency by conducting experiments on a set of grayscale images and then using PSNR as a measurement function, where the result was 67.9705.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 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