  This paper proposes a new defense approach for counteracting with
state-of-the-art white and black-box adversarial attack algorithms. Our
approach fits in the category of implicit reactive defense algorithms 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.
