Optimizing Multibit Spread Spectrum Audio Watermarking for Internet of Things
Bibliographic record
Abstract
The development of Internet has lead the technology to Internet of Things (IoT). As the Internet of Things is used for sending many types of data through many interconnected devices, the risk of stealing the data owner's right has been increased. But, even though this risk comes up, the high demand for sending data through IoT is still high. Thus, watermarking schemes is used for preventing this problem. Watermarking can be used for protecting the owner's rights in the form of a digital image, and this will be useful to be implemented in IoT. In this paper, an enhancement of Spread Spectrum (SS) based audio watermarking system against MP3 Compression attacks using Genetic Algorithm process is proposed. The main idea of the system will be using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) as the pre-processing method and then using Multibit Spread Spectrum to embed the watermark. The simulation results show that the Genetic Algorithm could optimize between imperceptibility and robustness resulting a good result against the MP3 Compression attack.
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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 | 0.000 |
| 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) | 0.001 | 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".