Signature based Fuzzy Vaults with Boosted Feature Selection
Bibliographic record
Abstract
Handwritten signatures are commonly employed in many financial and forensic processes, and secure offline signature verification systems (SV) are required to automate such processes. In this context, bio-cryptography systems based on the handwritten signatures may be considered for enhance security. This paper presents a bio-cryptography system that constructs Fuzzy Vaults (FVs) based on the offline signature images. Boosting Feature Selection is employed to select features while training weak classifiers of offline SV systems. The indexes of selected features correspond to the most stable and discriminant features from a user's signature images, and are used to encode user-specific FVs. A password is employed as a second authentication measure, to further enhance system security. During authentication, a user provides both the signature and the password to decode the FV and decouple his private key. If the FV is correctly decoded, the user is authenticated by the verification system. The proposed FV implementation alleviates the security vulnerabilities of the classical SV systems like template security, repudiation, irrevocability, and bypassing the classification decision. Moreover, simulations performed on a real-world signature verification database (with random, simple, and skilled forgeries) indicate security guarantees against stolen authentication measures. While compromised signatures or passwords lead to complete fail (FAR = 100%) of the classical SV or password protected cryptography systems respectively, compromised signatures lead to FAR of 0.1%, and compromised passwords leads to FAR of 15% with the proposed system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".