Forgery Detection in Hyperspectral Document Images using Graph Orthogonal Nonnegative Matrix Factorization
Bibliographic record
Abstract
The analysis of inks plays a crucial role in the examination process of questioned documents. To address this issue, we propose a new approach for ink mismatch detection in Hyperspectral document (HSD) images based on a new orthogonal and graph regularized Nonnegative Matrix Factorization (NMF) model. Although some previous works have proposed orthogonality constraints to solve clustering problems in different contexts, the application of such constraints is not straightforward due to the sum-to-one assumption related to the physical nature of Hyperspectral images. In this work, we demonstrate that under some acquisition protocols, latent factors in HSD images can be constrained to be orthogonal. We also incorporate a graph regularized term to exploit the geometric information lost by the matricization of HSD images. Furthermore, we propose an efficient alternating direction method of multipliers based algorithm to solve the proposed method. Our empirical validation demonstrates the competitiveness of the proposed algorithm compared to the state-of-the-art methods. It shows a high potential to be used as a reliable tool for quick investigation ofquestioned documents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".