An adaptive stochastic-resonance-based detector and its application in watermark extraction
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
In this paper, we explore a stochastic resonance (SR) based detector using bistable system (BS) to detect a binary pulse amplitude modulated (PAM) signal embedded in non-Gaussian noise. Through the example of BS based watermark extraction, we show that a reliable performance cannot be obtained if the BS parameters are determined by traditional tuning technique. The key observation is that the BS parameters are not sensitive to the pdf of the noise but to the variance of the noise and the amplitude of the signal. That makes it possible to determine the BS parameters in advance and an adaptive BS can be constructed based on the estimated amplitude of the watermark (signal) and the variance of the DCT coefficients (noise). Experimental results show that the performance obtained from the proposed adaptive stochastic-resonator-based detector is stable and provides superior performance compared to the existing BS based watermark schemes and the Gaussian based maximum likelihood (ML) detector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it