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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".