  Automatic speech recognition systems have created exciting possibilities for
applications, however they also enable opportunities for systematic
eavesdropping. We propose a method to camouflage a person's voice over-the-air
from these systems without inconveniencing the conversation between people in
the room. Standard adversarial attacks are not effective in real-time streaming
situations because the characteristics of the signal will have changed by the
time the attack is executed. We introduce predictive attacks, which achieve
real-time performance by forecasting the attack that will be the most effective
in the future. Under real-time constraints, our method jams the established
speech recognition system DeepSpeech 3.9x more than baselines as measured
through word error rate, and 6.6x more as measured through character error
rate. We furthermore demonstrate our approach is practically effective in
realistic environments over physical distances.
