An approach for securing audio classification against adversarial attacks

Adversarial audio attacks are small perturbations that are not perceivable by humans and are intentionally added to audio signals to impair the performance of machine learning (ML) models. These attacks raise serious concerns about the security of ML models, as they can cause them to make mistakes and ultimately generate wrong predictions.

* This article was originally published here