Towards automated transactions based on the offline handwritten signatures
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
Automating business transactions over the Internet relies on digital signatures, a replacement of conventional handwritten signatures in paper-based processes. Although they guarantee data integrity and authenticity, digital signatures are not as convenient to users as the manuscript ones. In this paper, a methodology is proposed to produce digital signatures using off-line hand-written signatures. This methodology facilitate the automation of business processes, where users continually employ their handwritten signatures for authentication. Users are isolated from the details related to the generation of digital signatures, yet benefit from enhanced security. First, signature templates from a user are captured and employed to lock his private key in a fuzzy vault. Then, when the user signs a document by hand, his handwritten signature image is employed to unlock his private key. The unlocked key produces a digital signature that is attached to the digitized document. The verification of the digital signature by a recipient implies authenticity of the manuscript signature and integrity of the signed document. Experimental results on the Brazilian off-line signature database (that includes various forgeries) confirms the viability of the proposed approach. Private keys of 1024-bits were unlocked by signature images with Average Error Rate of about 7.8%.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it