Digital Image Security Implementation With Uses Super Encryption Algorithm Myszkowski And The Algorithm Paillier Cryptosystem
Bibliographic record
Abstract
This study aims to implement digital image security by applying two encryption algorithms, namely the Myszkowski algorithm and the Paillier Cryptosystem algorithm. Digital images are a very important form of data and are used frequently in a variety of applications, so protecting their security is a major concern. The encryption method proposed in this study uses a combination of the Myszkowski algorithm to randomize image pixels and the Paillier Cryptosystem algorithm to perform symmetric key encryption. At the experimental stage, qualitative and quantitative analysis was carried out on the performance of the encryption implemented on digital images. Testing is carried out by comparing the level of security and encryption speed of the two algorithms used. In addition, size analysis of encrypted images was also performed to evaluate the efficiency of the proposed system. The results of the study show that the use of a combination of the Myszkowski algorithm and the Paillier Cryptosystem algorithm provides a high level of security for digital images. In addition, the efficiency of this system has also been proven in producing efficient encryption image sizes, so that it can be implemented in image-based applications that require a higher level of security.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".