Pengenalan Captcha dengan Multivalued Image Decomposition dan Vector Space Image Recognition
Bibliographic record
Abstract
Completely Automated Public Turing Tests to Tell Computers and Humans Apart (CAPTCHA) merupakan program untuk meningkatkan keamanan web. Pengenalan CAPTCHA menggunakan aplikasi sering mengalami kegagalan karena posisi dari simbol yang terlalu rapat, juga karena sulitnya melatih simbol baru jika gagal dikenali. Metode Naive Pattern Recognition Algorithm salah satu metode yang belum memberikan hasil yang maksimal karena kesalahan pada proses pengenalan simbol tidak dapat dilatih kembali sehingga aplikasi tetap tidak akan mengenali simbol tersebut. Metode Multivalued Image Decomposition dan Vector Space Image Recognition dapat memberikan hasil yang lebih maksimal dengan menggunakan Training Set, dimana simbol yang tidak dikenali akan dilatih/training agar proses pengenalan simbol selanjutnya lebih akurat. Pengujian dilakukan terhadap CAPTCHA dengan berbagai warna background, CAPTCHA dengan simbol yang saling berdekatan (menyatu) dan kombinasi warna simbol dengan background yang berbeda. Untuk CAPTCHA dengan simbol berukuran berbeda dan saling terhubung, tidak dapat dikenali. Dengan threshold 0.90, hasil pengujian dengan training set yang dilakukan terhadap dengan algoritma ini menunjukkan akurasi tingkat keberhasilan sebesar 87%.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".