Generating Off-line and On-line Forgeries from On-line Genuine Signatures
Bibliographic record
Abstract
Improving the ability of forgery detection in Automatic Signature Verifiers requires databases with an extensive and realistic set of skilled forgeries. However, collecting skilled professional copies is not always possible in many studies, and most the publicly available databases are limited from that point of view. Recently, the Kinematic Theory of Rapid Movements has been used to improve the skillfulness from a given on-line skilled forgery. This paper proposes a method based on the Kinematic Theory of Rapid Movements to generate both on-line and off-line skilled synthetic forgeries from a single on-line genuine specimen. The method consists of three steps: 1. The genuine on-line signature is decomposed as a sum of overlapped lognormal strokes, 2. The trajectory is modified by distorting the virtual target points and the lognormal parameters, and 3. A new velocity profile is automatically synthetized from the 8-connected modified trajectory. Experiments with multiple on-line and off-line signature verifiers show that this method can generate skilled forgeries harder to detect.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".