  In this paper we propose a novel defense approach against end-to-end
adversarial attacks developed to fool advanced speech-to-text systems such as
DeepSpeech and Lingvo. Unlike conventional defense approaches, the proposed
approach does not directly employ low-level transformations such as
autoencoding a given input signal aiming at removing potential adversarial
perturbation. Instead of that, we find an optimal input vector for a class
conditional generative adversarial network through minimizing the relative
chordal distance adjustment between a given test input and the generator
network. Then, we reconstruct the 1D signal from the synthesized spectrogram
and the original phase information derived from the given input signal. Hence,
this reconstruction does not add any extra noise to the signal and according to
our experimental results, our defense-GAN considerably outperforms conventional
defense algorithms both in terms of word error rate and sentence level
recognition accuracy.
