Toward a Robust Image Watermarking Method: Exploiting Human Visual System Properties in the Spatial Domain
Bibliographic record
Abstract
The need for secure and reliable methods to protect digital content from unauthorized manipulation has become increasingly critical.Although cryptography has been instrumental in addressing digital content security, it is not without shortcomings.In particular, symmetrical cryptographic systems exhibit significant vulnerabilities in encryption key protection, while asymmetric cryptosystems demand high computational time.Consequently, there is an urgent need to explore alternative security solutions to address these limitations.Watermarking has emerged as a promising candidate for ensuring integrity, authenticity, and digital rights protection.In this study, a novel, robust, and informed watermarking approach for color digital images in the spatial domain is proposed, leveraging Local Binary Pattern (LBP) operators for watermark generation.The embedding of the watermark is achieved in the blue channel of the Red, Green, Blue (RGB) image on its corresponding LBP image, owing to the low sensitivity of the blue color in relation to the imperceptibility of the embedded watermark.The proposed approach is evaluated against a diverse range of geometric and non-geometric attacks to demonstrate its reliability.In order to assess the performance of the proposed method in terms of imperceptibility and robustness, watermarked images are subjected to various attacks and analyzed using well-established metrics.The results obtained are highly encouraging, both in terms of the imperceptibility of the embedded watermarks and their robustness against different attack scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".