  Deep learning based voice synthesis technology generates artificial
human-like speeches, which has been used in deepfakes or identity theft
attacks. Existing defense mechanisms inject subtle adversarial perturbations
into the raw speech audios to mislead the voice synthesis models. However,
optimizing the adversarial perturbation not only consumes substantial
computation time, but it also requires the availability of entire speech.
Therefore, they are not suitable for protecting live speech streams, such as
voice messages or online meetings. In this paper, we propose VSMask, a
real-time protection mechanism against voice synthesis attacks. Different from
offline protection schemes, VSMask leverages a predictive neural network to
forecast the most effective perturbation for the upcoming streaming speech.
VSMask introduces a universal perturbation tailored for arbitrary speech input
to shield a real-time speech in its entirety. To minimize the audio distortion
within the protected speech, we implement a weight-based perturbation
constraint to reduce the perceptibility of the added perturbation. We
comprehensively evaluate VSMask protection performance under different
scenarios. The experimental results indicate that VSMask can effectively defend
against 3 popular voice synthesis models. None of the synthetic voice could
deceive the speaker verification models or human ears with VSMask protection.
In a physical world experiment, we demonstrate that VSMask successfully
safeguards the real-time speech by injecting the perturbation over the air.
