A simple image-adaptive watermarking algorithm with blind extraction
Bibliographic record
Abstract
In this paper, we propose an adaptive, DCT based, blind image watermarking algorithm. The adaptability is developed, while exploring the properties of the human visual system (HVS), to embed robust watermarks in DCT matrices while making them the least visible in the space domain. In the algorithm, binary watermarks are embedded in image blocks having gray level variations sufficient to mask a certain level of signal alterations. The orientations of the variations in such a block are detected by examining the locations of the most significant DCT coefficients in the matrix and a particular DCT coefficient is then chosen for watermark embedding with a view to making the gray level changes resulting from the embedding have a similar orientation with the original image signal to mask the watermark better. Moreover, the embedding strength is made to depend on the DC component, as it indicates the brightness of the image block, to further reduce the visibility of the watermark. The algorithm is computationally simple because it extracts the needed features directly from the DCT coefficients. Simulation results show that the algorithm is highly robust against JPEG compression even at low quality factors while maintaining high visual quality. Furthermore, the computational simplicity of the algorithm makes it very useful in real-time applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".