KEAMANAN CITRA DIGITAL DENGAN MEMANFAATKAN PROSES PENERAPAN ALGORITMA DATA ENCRYPTION STANDART (DES) PADA EKTRAKSI PIXEL
Bibliographic record
Abstract
In today's digital technology, almost all activities can be documented in the form of digital images. The problem that arises is that images that are confidential can be stolen and accessed by people who are not entitled. To overcome this security problem, image files can be encrypted using cryptographic algorithms. One cryptographic algorithm that can be used is the Data Encryption Standard (DES) algorithm. This algorithm is a block cipher with a size of 64 bits or 8 bytes. Therefore, DES is used to encrypt image files per 8 bytes. Each encryption process will pass 16 cycles and produce cipher bytes. The result of encryption is an image file with an unrecognized format and cannot be opened because the contents of the byte file have been randomized. To return the scrambled file back to the original image file, perform the decryption process using the same key as the encryption key. The application can be used to encrypt and decrypt images using the DES algorithm. The application can also display the steps of the encryption and decryption process towards the contents of the byte in the 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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".