Implementation Of Affine Cipher Combination And Merkle Hellman On The Process 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
The use of image media information has several weaknesses, one of which is the ease with which it can be manipulated by irresponsible people. Efforts made in increasing the security of this image is cryptography, namely the science and art of maintaining the security and confidentiality of images. Cryptography is used so that the confidentiality of the image can be maintained, so that it is not known by others. In general, there are 2 (two) techniques for carrying out cryptographic processes, namely encryption and decryption. In this study, the encryption process was first carried out using the Affine Cipher algorithm and then continued with the Merkle Hellman algorithm, while the decryption was carried out first by Merkle Hellman and then decrypted again with the Affine Cipher. The results of this study indicate that applying the Affine Cipher and Merkle Hellman algorithms can secure images. At the end of this system is an image that is in the form of blur so that it is not known and not understood by others. That way people who are not entitled cannot understand from the image.
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