Adaptive, scalable and robust watermarking for wavelet-based progressive image transmission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Image watermarking refers to the process of embedding an authentication message, called watermark, into the host image to uniquely identify the ownership. In this paper, an adaptive, scalable, blind and robust wavelet-based watermarking approach is proposed. The proposed approach enables scalable watermark detection and provides robustness against progressive wavelet image compression. A multiresolution decomposition of the binary watermark is inserted into the selected coefficients of the wavelet-decomposed image that represent the high activity regions of the image. The watermark insertion is started from the lowest frequency sub-band of the decomposed image and each decomposed watermark sub-band is inserted into its counterpart sub-band of the decomposed image. In the lowest frequency sub-band, coefficients with maximum local variance and in the higher frequency sub-bands, coefficients with maximum magnitude are selected. The watermarked test images are transparent according to the human vision system characteristics and do not show any perceptual degradation. The experimental results very efficiently prove the robustness of the approach against progressive wavelet image coding even at very low bit rates. The watermark extraction process is completely blind and multiple spatial resolutions of the watermark are progressively detectable from the compressed watermarked image. This approach is a suitable candidate for providing efficient authentication for progressive image transmission applications especially over heterogeneous networks, such as the Internet.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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 it