A Robust Approach for Securing Audio Classification Against Adversarial\n Attacks
Bibliographic record
Abstract
Adversarial audio attacks can be considered as a small perturbation\nunperceptive to human ears that is intentionally added to the audio signal and\ncauses a machine learning model to make mistakes. This poses a security concern\nabout the safety of machine learning models since the adversarial attacks can\nfool such models toward the wrong predictions. In this paper we first review\nsome strong adversarial attacks that may affect both audio signals and their 2D\nrepresentations and evaluate the resiliency of the most common machine learning\nmodel, namely deep learning models and support vector machines (SVM) trained on\n2D audio representations such as short time Fourier transform (STFT), discrete\nwavelet transform (DWT) and cross recurrent plot (CRP) against several\nstate-of-the-art adversarial attacks. Next, we propose a novel approach based\non pre-processed DWT representation of audio signals and SVM to secure audio\nsystems against adversarial attacks. The proposed architecture has several\npreprocessing modules for generating and enhancing spectrograms including\ndimension reduction and smoothing. We extract features from small patches of\nthe spectrograms using speeded up robust feature (SURF) algorithm which are\nfurther used to generate a codebook using the K-Means++ algorithm. Finally,\ncodewords are used to train a SVM on the codebook of the SURF-generated\nvectors. All these steps yield to a novel approach for audio classification\nthat provides a good trade-off between accuracy and resilience. Experimental\nresults on three environmental sound datasets show the competitive performance\nof proposed approach compared to the deep neural networks both in terms of\naccuracy and robustness against strong adversarial attacks.\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".