This paper proposes a new defense approach for counteracting state-of-the-art white and black-box adversarial attack algorithms. Our approach fits into the implicit reactive defense algorithm category since it does not directly manipulate the potentially malicious input signals. Instead, it reconstructs a similar signal with a synthesized spectrogram using a cyclic generative adversarial network. This cyclic framework helps to yield a stable generative model. Finally, we feed the reconstructed signal into the speech-to-text model for transcription. The conducted experiments on targeted and non-targeted adversarial attacks developed for attacking DeepSpeech, Kaldi, and Lingvo models demonstrate the proposed defense's effectiveness in adverse scenarios.
@article{arxiv.2103.14717,
title = {Cyclic Defense GAN Against Speech Adversarial Attacks},
author = {Mohammad Esmaeilpour and Patrick Cardinal and Alessandro Lameiras Koerich},
journal= {arXiv preprint arXiv:2103.14717},
year = {2021}
}