Application Of Super Encryption Using Rot 13 Algorithm Method and Algorithm Beaufort Cipher For Image Security Digital
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
Digital image security is becoming increasingly critical in today's digital era, where sensitive information and data are often stored in image form. Therefore, an effective and secure encryption method is needed to protect the integrity and confidentiality of digital images. This study aims to implement a stronger security approach by combining classic encryption methods, namely the ROT13 algorithm and the Beaufort Cipher algorithm which produces an encryption called "Super Encryption". In this study, first of all, the ROT13 encryption method will be applied to randomize digital image text by shifting characters as far as 13 positions in the alphabet. Then, the Beaufort Cipher algorithm will be used to apply additional encryption to the digital image, which involves using the key as input in the encryption process. The results of this study indicate that the Super Encryption method which combines the ROT13 and Beaufort Cipher algorithms provides a higher level of security compared to using each method separately. Security testing and vulnerability analysis show that the combination of these two algorithms produces digital images that are more difficult to decrypt by commonly used decryption attacks.
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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 | 0.001 | 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