Enhanced Security and Robustness in Color Image Watermarking Using DWT-HD and Quaternion SVD under Hybrid Attacks
Bibliographic record
Abstract
Securing color images through watermarking presents significant challenges, particularly when subjected to complex hybrid attacks.The objective of this study is to enhance both the security and robustness of Color Image Watermarking (CIW) by developing a novel method that integrates Discrete Wavelet Transform (DWT) and Quaternion Singular Value Decomposition (QSVD) with Heisenberg Decomposition (HD).This combined approach, referred to as DWT-QSVD-HD, has been tested rigorously under various hybrid attack scenarios.The method employs a dual-key strategy, utilizing a logo watermark and the HH component of the R-level DWT, to enhance robustness.A modified watermark insertion rule is proposed, leveraging both keys simultaneously within the HD-QSVD domain, which significantly complicates unauthorized decoding efforts.The LAB color space is utilized during the pre-processing stage to maximize entropy, with entropy analysis providing justification for this adaptation.To further bolster security, the watermarked image is encrypted using a simplified fast AES algorithm, adding an additional layer of protection and improving the watermark's resilience.The performance of the proposed method is evaluated against existing techniques using parametric analysis, optimized via the Fruit Fly Optimization Algorithm (FOA) for key metrics such as Normalized Correlation (NC), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM).The method demonstrates exceptional performance across various image sizes, maintaining an NC above 0.995 for 64×64 images.Notably, under motion blur attacks, the proposed method improves the NC from 0.8322 to 0.9003 for 256×256 images.The impact of individual and hybrid watermark attacks is systematically assessed, with results showing superior extraction and recovery of image quality when using the proposed technique.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".