Combining cryptography and watermarking to secure revocable iris templates
Bibliographic record
Abstract
Biometric cryptosystems have recently evolved as a means for solving key management issues as well as protecting biometric templates. In this paper, we propose the combination of cryptography with Least Significant Bit — Discrete Wavelet Transform (LSB-DWT) watermarking to secure iris templates. The key-binding bio-cryptosystem is based on fuzzy sketches that handle intra-class variability by using error correction codes. Hadamard and Reed-Solomon codes are used to correct both random and burst errors that occur in iris codes. To achieve revocability a user-specific iris shuffling algorithm is used. We used the CASIA iris database in our experiments and were able to retrieve a 210 bit key with 0 False Acceptance Rate (FAR) and 0.07% False Rejection Rate (FRR). The proposed system is also capable of withstanding minor spatial and frequency watermarking attacks without major degradation in the performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".