Intelligent Watermarking with Multi-objective Population Based Incremental Learning
Bibliographic record
Abstract
Intelligent watermarking techniques use nonconventional methods like Evolutionary Computation techniques to satisfy the trade-off between integrity and authenticity of digitized documents. In this paper, we propose a multi-objective Population Based Incremental Learning module for an intelligent watermarking system for grayscale images to optimize embedding watermarks that satisfy the trade-off between quality and robustness. The multi-objective formulation provides set of non-dominated solutions rather than single solution, which allows tuning the quality and robustness for several attacks, without the need for an expensive re-optimization process due to changing the priority of different objectives after the optimization process. Different formulations for the optimization problem were investigated to achieve best fitness for different objectives. The best fitness achieved for all objectives are compared for different formulations. Simulation results indicate better fitness for different objectives with multi-objective formulation, and faster convergence using incremental learning techniques.
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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 | 0.001 |
| 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".