Cyclic Defense GAN Against Speech Adversarial Attacks

Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich

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.

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