A Robust Color Image Watermarking Scheme Based on Discrete Wavelet Transform Domain and Discrete Slantlet Transform Technique
Bibliographic record
Abstract
Watermarking is primarily used to prevent unauthorized copying of digital data by inserting a watermark as a symbol of ownership in the digital data. The purpose of this work is to increase the imperceptibility and robustness of watermarked image by utilizing the Discrete Wavelet Transform (DWT) domain and Discrete Slantlet Transform (DST) technique. In compared to existing methodologies, the strategies used in this work considerably increased the robustness, imperceptibility, and protection of the watermarked image against JPEG compression and other types of noise attacks. The proposed method's robustness was determined by comparing the image's Normalized Cross-Correlation (NCC) value before and after the watermarking operation. The proposed method increased the NCC value to greater than 0.7 and the Peak Signal-to-Noise Ratio (PSNR) value to greater than 59 decibels (dB). Additionally, the proposed approach does not require knowledge of the original image in order to extract the watermarks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".