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Reverse Adversarial Attack To Enhance Environmental Sound Classification

Classification of environmental sounds becomes an essential unit for several applications such as robot audition, audio-visual and ambient intelligent systems. Recent years have witnessed remarkable success of adversarial attacks for misguiding deep models with a high fooling rate. Adversarial examples crafted with adversarial techniques pose serious security threats for deployment of deep models to practical scenarios. In this paper, we show that adversarial signals crafted by performing search in the opposite direction of the search direction of the perturbations could be utilized while training of deep classifiers of environmental sound classification (ESC). The proposed reverse adversarial attack (RAA) technique can be used as data augmentation tool to enhance the accuracy of deep model that performs ESC. The deep network trained with the crafted adversarial signal shows high robustness against adversarial attacks. The efficacy of the proposed method is evaluated on two benchmark ESC datasets viz. ESC-10 and DCASE-2019 Task-1A datasets. The experiments and results show that the model trained with the proposed technique shows comparable and competitive performance to state-of-the-art (SOTA) deep ESC models.