ASR (automatic speech recognition) systems like Siri, Alexa, Google Voice or Cortana have become quite popular recently. One of the key techniques enabling the practical use of such systems in people's daily life is deep learning. Though deep learning in computer vision is known to be vulnerable to adversarial perturbations, little is known whether such perturbations are still valid on the practical speech recognition. In this paper, we not only demonstrate such attacks can happen in reality, but also show that the attacks can be systematically conducted. To minimize users' attention, we choose to embed the voice commands into a song, called CommandSong. In this way, the song carrying the command can spread through radio, TV or even any media player installed in the portable devices like smartphones, potentially impacting millions of users in long distance. In particular, we overcome two major challenges: minimizing the revision of a song in the process of embedding commands, and letting the CommandSong spread through the air without losing the voice "command". Our evaluation demonstrates that we can craft random songs to "carry" any commands and the modify is extremely difficult to be noticed. Specifically, the practical CommandSongs over the air can achieve 94 percentage success rate.
Recently, CommandSong REF was proposed to embed a set of commands into a song, to spread to a large amount of audience.
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
ArXiv
ArXiv
Computer Science Engineering