Correlation-and-Bit-Aware Spread Spectrum Embedding for Data Hiding
Bibliographic record
Abstract
This paper proposes a correlation-and-bit-aware concept for data hiding by exploiting the side information at the encoder side, and we present two improved data hiding approaches based on the popular additive spread spectrum embedding idea. We first propose the correlation-aware spread spectrum (CASS) embedding scheme, which is shown to provide better watermark decoding performance than the traditional additive spread spectrum (SS) scheme. Further, we propose the correlation-aware improved spread spectrum (CAISS) embedding scheme by incorporating SS, improved spread spectrum (ISS), and the proposed correlation-and-bit-aware concept. Compared with the traditional additive SS, the proposed CASS and CAISS maintain the simplicity of the decoder. Our analysis shows that, by efficiently incorporating the side information, CASS and CAISS could significantly reduce the host effect in data hiding and improve the watermark decoding performance remarkably. To demonstrate the improved decoding performance and the robustness by employing the correlation-and-bit-aware concept, the theoretical bit-error performances of the proposed data hiding schemes in the absence and presence of additional noise are analyzed. Simulation results show the superiority of the proposed data hiding schemes over traditional SS schemes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".