An Improved Multiplicative Spread Spectrum Embedding Scheme for Data Hiding
Bibliographic record
Abstract
This paper presents an improved multiplicative spread spectrum (IMSS) embedding scheme for data hiding. We first analyze the error probability of the conventional multiplicative spread spectrum (MSS) scheme and derive the corresponding channel capacity and security level. It is noted that the interference effect of the host signal causes the distribution leakage and contributes to the decoding performance degradation. Since the host signal and the decoder structure information are available at the encoder side, the proposed IMSS scheme exploits both the correlation between the host signal and the watermark signal and the decoder structure in embedding the bit information to reduce the host interference effect. We can show that, compared with MSS, the proposed IMSS maintains the simple decoder structure and does not require additional information for decoding. We also analyze the decoding performances of MSS and IMSS in the presence of additional Gaussian noise. Simulation and real image results illustrate the superiority of the proposed IMSS data hiding scheme over the conventional MSS scheme.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".