Audio Watermarking Scheme Using IMFs and HHT for Forensic Applications
Bibliographic record
Abstract
Audio signals and applications are numerous and ubiquitous. Most of these applications especially those on the Internet require authentication and proof(s) of ownership. There are several efficient methods in the literature address these crucial and critical concerns. In this paper, the authors present a new non-blind audio watermarking scheme for forensic audio authentication and proof of ownership. The proposed scheme is based on empirical mode decomposition and Hilbert Haung Transformation (HHT). In this method, the audio signal is decomposed into frames of 1024 sample each. These frames are further decomposed into its several mono-component signals called Intrinsic Mode Functions (IMF). These Intrinsic Mode Functions will serve as the addressee for the watermark. In this research, the chosen watermark is a pseudo random number generated by Matlab-7, which is added to the highest and lowest IMFs of each frame of the decomposed signal. This is done to accommodate for time scale modification attacks as well as MP3 compression respectively. Experimental results show that the watermarked audio signals maintained high fidelity of more than 20 dBs which meets the International Federation of Phonographic Industry requirements. The results also show that the proposed scheme is robust against signal processing attacks such as MP3, time scale modification, and resizing attacks.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".