Adversarial robustness evaluation of representation learning models and universal audio representations

Source code for the paper "Adversarial Robustness Evaluation of Representation Learning for Audio Classification".

Setup

Use conda and the environment files provided as specified:

Dataset_import: Download, import and decompress the HEAR tasks and datasets.
Resample: Resample the audio files to target sampling rates.
Model_import: Import the HEAR models, compute and evaluate the embeddings.
Main_Loop: Perform the attacks, evaluate and present the results.
SVM: Perform the SVM-based detection of adversarial examples and present the results.

The results are presentend in the notebooks.
For a direct access the two zip files contain the final results for the Attack and SVM phases.

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
Dataset_import.ipynb		Dataset_import.ipynb
Main_Loop.ipynb		Main_Loop.ipynb
Model_import.ipynb		Model_import.ipynb
README.md		README.md
Resample.ipynb		Resample.ipynb
SVM.ipynb		SVM.ipynb
attackPipeline.yml		attackPipeline.yml
datasets.json		datasets.json
hearPipeline.yml		hearPipeline.yml
results.zip		results.zip
results_SVM.zip		results_SVM.zip
savedModelsName.json		savedModelsName.json