Robust Audio Adversarial Example for a Physical Attack

The success of deep learning in recent years has raised concerns about adversarial examples, which allow attackers to force deep neural networks to output a specified target. Although a method by which to generate audio adversarial examples targeting a state-of-the-art speech recognition model has been proposed, this method cannot fool the model in the case of playing over the air, and thus, the threat was considered to be limited. In this paper, we propose a method to generate adversarial examples that can attack even when playing over the air in the physical world by simulating transformation caused by playback or recording and incorporating them in the generation process. Evaluation and a listening experiment demonstrated that audio adversarial examples generated by the proposed method may become a real threat.