  An automatic speech recognition (ASR) system based on a deep neural network
is vulnerable to attack by an adversarial example, especially if the
command-dependent ASR fails. A defense method against adversarial examples is
proposed to improve the robustness and security of the ASR system. We propose
an algorithm of devastation and detection on adversarial examples that can
attack current advanced ASR systems. We choose an advanced text- and
command-dependent ASR system as our target, generating adversarial examples by
an optimization-based attack on text-dependent ASR and the GA-based algorithm
on command-dependent ASR. The method is based on input transformation of
adversarial examples. Different random intensities and kinds of noise are added
to adversarial examples to devastate the perturbation previously added to
normal examples. Experimental results show that the method performs well. For
the devastation of examples, the original speech similarity after adding noise
can reach 99.68%, the similarity of adversarial examples can reach zero, and
the detection rate of adversarial examples can reach 94%.
