DCT-based Robust Reversible Watermarking Technique based on histogram Modification
Bibliographic record
Abstract
In this paper, a strong, reversible image watermarking technique based on discrete cosine transform (DCT) and histogram shifting is proposed, where it overcomes the following concerns: (i) Reversing the cover object to its starting appearance is the primary goal of the reversible watermarking system. (ii) Military, medical, and standard law enforcement images are the main types of images that require distortion and reinstatement of the cover object following the watermark extraction. (iii) Lack of robustness and cover image-dependent embedding capacity are the primary concerns about reversible watermarking. Decompose the cover object into blocks that don't overlap in the first stage to insert a binary watermark bit into every block that is converted. These binary bits of watermark are embedded by altering a single set of middle substantial AC coefficients. To restore the cover image, subsequently using the histogram bin shifting method, a location map is created and integrated within the cover image. On the extracting side, at first, a location map is extracted from the image using the histogram bin shifting technique. In the following step, the image's watermark is recovered, and a reversed image has been generated using a location map. To verify the robustness property, several image processing attacks are tested with the suggested reversible watermarking approach, and favorable results are attained. The proposed scheme using the Lena image achieved 46.62 imperceptibility for 4096 embedding capacities. To methodically evaluate the proposed approach, it is compared with two current reversible watermarking systems, where they achieved 39.10 and 37.90 imperceptibility with 4.4 × 103 and 256 embedding capacities, respectively. The experimental results affirmed that the suggested method exhibits superior performance relative to these existing techniques.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".