Detection of Transcribed Seal Impressions Using 3‐D Pressure Traces
Bibliographic record
Abstract
Seals have been frequently used to certify that individuals or organizations have authorized or approved a document that bears these impressions. Much attention has been focused on the detection of forged seal impressions to expose and prevent seal-related frauds. This paper describes an image-processing technique that detects seal impressions transferred from a genuine document to a target document using transcription media. The proposed method utilizes a three-dimensional (3-D) scanner to generate a pressure trace map of the suspect seal impression. After utilizing a noise-reduction algorithm to improve image quality, the pressure map is aligned with a 2-D image of the same seal impression. The pressure ratio, determined by comparing the pressure map and inked impression of a suspect seal, can be used to determine whether the seal is genuine or was transferred to the target document. The results show that the proposed technique successfully identified transcribed seal impressions with an error rate of <1%.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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".