Pengembangan Aplikasi Steganografi pada Citra dengan Metode Blowfish dan Sequential Colour Cycle
Bibliographic record
Abstract
Perkembangan teknologi yang semakin pesat, membuat sebuah perjalanan data melalui internet menjadi tidak aman. Untuk menjaga keamanan sebuah data tersebut, dibuatlah sebuah teknik pengamanan data menggunakan kriptografi. Namun, pengamanan dengan kriptografi masih membuat pihak ketiga menjadi curiga karena perubahan data masih terlihat. Untuk itu dilakukanlah kombinasi kriptografi dengan steganografi. Pada penelitian ini, algoritma kriptografi yang digunakan adalah algoritma Blowfish. Informasi yang telah dienkripsi dengan menggunakan algoritma Blowfish tersebut dimasukkan ke dalam media gambar dengan algoritma Steganografi, dalam hal ini algoritma steganografi yang digunakan adalah Sequential Colour Cycle. Kombinasi algoritma Blowfish dan Sequential Colour Cycle ini dapat memberikan peningkatan kualitas pengamanan pada suatu media gambar, sehingga informasi yang terdapat di dalam media gambar tidak diketahui oleh orang yang tidak di inginkan.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".