  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.
