How to detect problematic signers for automatic signature verification
Bibliographic record
Abstract
Automatic Signatures Verification (ASV) is a pattern recognition application where the patterns are obviously the signatures of different signers. To correctly characterize a given signature, one must use several specimens from each signer and therefore, the intrinsic variability of some signers become a fundamental issue. Another fundamental issue of ASV system design is the unpredictable nature of true forgeries. This emphasizes the problem already mentioned but also points out another aspect of ASV systems that is usually not taken into account by system designer: the intrinsic difficulty of signatures to be Imitated by potential forgers. An experiment specifically designed on signature imitations has been conducted. Eight subjects were trained with the help of an electro-acoustical set-up in order to imitate, both visually and dynamically, eight reference signatures. First, these signatures have been analyzed to estimate the difficulty of each one to be imitated. This has been done with the help of a parametric coefficient based on data from experimental psychology and handwriting generation models. These signatures have also been analyzed to estimate their intraclass dissimilarity by an automatic comparison algorithm. The results of the experiment show to some extent, that problematic signers are those with signatures which are both instable (high intrinsic variability) and easy to imitate
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".