Impact of watermarking on offline signature verification in intelligent bio-watermarking systems
Bibliographic record
Abstract
Bio-watermarking systems were introduced as the synergistic integration of biometrics and digital watermarking to assure the integrity, authenticity and confidentiality of digitized image documents, and biometric templates. In this paper, the impact of watermarking attacks on the performance of offline signature verification is assessed in the context of intelligent bio-watermarking systems. The considered system is based on incremental learning computational intelligence, and multi-objective formulation that allows optimizing parameters according to watermark quality and robustness simultaneously. In this study, Extended Shadow Code features are extracted from digitized offline signatures, collected into feature vectors, and discretized into binary watermarks prior to being embedded into high resolution grayscale face image. The impact on biometric verification performance of quantization and different intensities of attacks are considered, and also observed the impact of using only certain areas of face images of higher texture Region Of Interest (ROI) for embedding the watermark. Experimental results conclude the optimal discretization, and better watermark fitness and verification performance when embedding in ROI. To improve the performance in future research, the authors propose to embed more reference signatures, use efficient ROI identification techniques, and finally novel formulation to add biometrics verification fitness to the watermark quality and robustness fitness during embedding optimization. The proposed system can be applied for verifying individuals crossing borders using offline signatures, or protecting biometric templates.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".