A dynamic fuzzy logic approach to adaptive HVS-based watermarking
Bibliographic record
Abstract
A novel image watermarking algorithm is introduced that consists of an adaptive watermarking algorithm based on a human visual system (HVS) model and a dynamic fuzzy inference system (DFIS). This scheme relies on the DFIS to extract the human eye sensitivity knowledge using the HVS model. The DFIS and the HVS combined are used to adjust and select the appropriate watermark length as well as the watermark strength for each pixel in an image. The main goal of the algorithm is to provide a more robust and imperceptible watermark. The aforementioned has been implemented and tested under various attacks including image compression, cropping, additive Gaussian noise distortion, scaling, low-pass filtering, as well as collusion attacks. The results achieved demonstrate that the watermark can survive these attacks while remaining imperceptible. The primary contribution of this adaptive watermarking approach is to further enhance spread-spectrum based watermarking schemes by introducing a DFIS model that encompasses a dynamic membership function engine to accurately approximate the relationship found between all properties of the HVS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".