A New Block Based Non-Blind Hybrid Color Image Watermarking Approach Using Lifting Scheme and Chaotic Encryption Based on Arnold Cat Map
Bibliographic record
Abstract
Online platforms became preferred mode of communication due to advancement in communication technology. Sharing of digital documents over online communication medium grown exponentially and thus demanded a secure, robust and transparent watermarking technique for authenticity of digital media and copyright protection. This research study proposes a robust and secure non-blind SVD-LWT watermarking technique. Color images are employed instead of gray scale images and Y channel of YCbCr color model is utilized to embed secret digital information. The selected color model is in accordance with the human visual system and Y channel is ideal for data hiding. Two level LWT, SVD is used and diagonal matrix of Y channel of host (cover) and watermark image along with scaling factor (α) is used to embed digital data. Block based and chaotic image encryption transform are used for image scrambling. The performances of presented watermarking scheme evaluated with the aid of fidelity parameters namely MSE, PSNR, SSIM and NCC.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".