Assessing the Vulnerability of Self-Supervised Speech Representations for Keyword Spotting Under White-Box Adversarial Attacks
Bibliographic record
Abstract
Self-supervised speech pre-training has emerged as a useful tool to extract representations from speech that can be used across different tasks. While these models are starting to appear in commercial systems, their robustness to so-called adversarial attacks have yet to be fully characterized. This paper evaluates the vulnerability of three self-supervised speech representations (wav2vec 2.0, HuBERT and WavLM) to three white-box adversarial attacks under different signal-to-noise ratios (SNR). The study uses keyword spotting as a downstream task and shows that the models are very vulnerable to attacks, even at high SNRs. The paper also investigates the transferability of attacks between models and analyses the generated noise patterns in order to develop more effective defence mechanisms. The modulation spectrum shows to be a potential tool for detection of adversarial attacks to speech systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".