A Combination Of A Rail Fence Cipher And Merkle Hellman Algorithm For Digital Image Security
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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