A comparative study of audio/speech steganalysis techniques
Bibliographic record
Abstract
This paper presents a comparative study of four steganalysis techniques for speech/audio files. The Mel-Frequency Cepstral Coefficients (MFCCs) are used for the acoustical analysis of the audio files. The following steganalyzers are assessed: Support Vector Machines (SVMs), Gaussian Mixture Models (GMMs), Deep Belief Networks (DBNs) and Recurrent Neural Networks (RNNs). These steganalysis methods are tested on three different steganographic approaches. Our experiments were carried out by using three steganographic techniques, namely, StegHide, Hide4PGP and FreqSteg that were applied to the Noizeus corpus. The results showed that the GMMs-based technique performed the best by reaching a perfect classification rate, without any error, followed by the SVMs, DBNs and RNNs.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".