A Combination Of A Rail Fence Cipher And Merkle Hellman Algorithm For Digital Image Security
Bibliographic record
Abstract
Image is a combination of planes, points, lines and colors to create a physical or human object. Images can be in the form of 2-dimensional images, such as photographs and paintings. 3-dimensional image like a statue. The use of image media information has several weaknesses, one of which is the ease with which it can be manipulated by certain parties with the help of increasingly developing technology. In this study, the Rail Fence Cipher and Merkle Hellman methods were applied which aimed to obtain a stronger cipher by utilizing two key levels where an asymmetric algorithm was used to protect the symmetric key. The asymmetric algorithm used is Merkle Hellman and the symmetrical algorithm used is Rail Fence Cipher. The results of this study indicate that applying the Rail Fence Cipher and Merkle Hellman algorithms can secure image files and secure keys for data integrity. Encryption and description processing time is affected by the size and resolution of the image file.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".