Combining cryptography and watermarking to secure revocable iris templates
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
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.
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 | 0.000 |
| 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