Text Encryption by Indexing ASCII of Characters Based on the Locations of Pixels of the Image
Bibliographic record
Abstract
Network security has recently become a major issue since the growth of electronic data exchange so cryptography is important in protecting secure online data resources from integrity, confidentiality, and safety perspective against potential attacks such as eavesdropping and brute force.In this paper, we proposed a method for encrypting the transmitted information based on an image, which worked as a key that is saved by the client and the server.The encryption process of the text will be to encode characters by changing the ASCII code of characters with the locations (row and column) of the ASCII code equivalent in the image data, the locations will be chosen randomly.The proposed algorithm provides a relatively greater degree of security in avoiding avalanches, eavesdropping attacks, and password space because the character encoding method will be dynamic depending on the size and type of image used.Several securities analyses were presented, and the proposed algorithm proved to be highly secure.Compared to some current text cypher schemes, the proposed algorithm is very safe against modern cryptanalysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.002 |
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".