Designing a Model for Hiding Images in RGB Cover Image Based Scrambling and Encryption Methods
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 (44) 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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".