New AI Could Prevent Eavesdropping By Disguising Words With Custom Noise
sciencehabit shares a report from Science Magazine: Big Brother is listening. Companies use "bossware" to listen to their employees when they're near their computers. Multiple "spyware" apps can record phone calls. And home devices such as Amazon's Echo can record everyday conversations. A new technology, called Neural Voice Camouflage, now offers a defense. It generates custom audio noise in the background as you talk, confusing the artificial intelligence (AI) that transcribes our recorded voices. The new system uses an "adversarial attack." The strategy employs machine learning -- in which algorithms find patterns in data -- to tweak sounds in a way that causes an AI, but not people, to mistake it for something else. Essentially, you use one AI to fool another. The process isn't as easy as it sounds, however. The machine-learning AI needs to process the whole sound clip before knowing how to tweak it, which doesn't work when you want to camouflage in real time. So in the new study, researchers taught a neural network, a machine-learning system inspired by the brain, to effectively predict the future. They trained it on many hours of recorded speech so it can constantly process 2-second clips of audio and disguise what's likely to be said next. For instance, if someone has just said "enjoy the great feast," it can't predict exactly what will be said next. But by taking into account what was just said, as well as characteristics of the speaker's voice, it produces sounds that will disrupt a range of possible phrases that could follow. That includes what actually happened next; here, the same speaker saying, "that's being cooked." To human listeners, the audio camouflage sounds like background noise, and they have no trouble understanding the spoken words. But machines stumble. The work was presented in a paper last month at the International Conference on Learning Representations, which peer reviews manuscript submissions.
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