Image Watermarking with Region of Interest Determination Using Deep Neural Networks
Bibliographic record
Abstract
Watermarking is a popular technique used in various applications, such as copyright protection of digital media, including audio, video, and image files. Proper watermarking should satisfy multiple criteria, such as robustness and transparency. While a successful watermarking needs to meet these criteria, there is a tradeoff between the two opposing criteria of robustness and transparency. This paper proposes a method for determining the appropriate locations for embedding watermarks with high strength factors. For this purpose, a deep neural network, known as Mask R-CNN, is used, which is pre-trained on the COCO dataset. This neural network finds a good strength factor for those sub-blocks of the host image selected for embedding. The proposed technique can be used in conjunction with most DWT and DCT based semi-blind watermarking approaches. Experiments show that the proposed method is robust against different attacks and demonstrates good transparency.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".