<p>With the advance of the
technologies of Internet of things, smart devices or virtual personal
assistants at home, such as Google Assistant, Apple Siri, and Amazon Alexa,
have been widely used to control and access different objects like door lock,
blobs, air conditioner, and even bank accounts, which makes our life
convenient. Because of its ease for operations, voice control becomes a main
interface between users and these smart devices. To make voice control more
secure, speaker verification systems have been researched to apply human voice
as biometrics to accurately identify a legitimate user and avoid the illegal
access. In recent studies, however, it has been shown that speaker verification
systems are vulnerable to different security attacks such as replay, voice
cloning, and adversarial attacks. Among all attacks, adversarial attacks are
the most dangerous and very challenging to defend. Currently, there is no known
method that can effectively defend against such an attack in speaker verification
systems.</p>
<p>The
goal of this project is to design and implement a defense system that is
simple, light-weight, and effectively against adversarial attacks for speaker
verification. To achieve this goal, we study the audio samples from adversarial
attacks in both the time domain and the Mel spectrogram, and find that the
generated adversarial audio is simply a clean illegal audio with small
perturbations that are similar to white noises, but well-designed to fool
speaker verification. Our intuition is that if these perturbations can be
removed or modified, adversarial attacks can potentially loss the attacking
ability. Therefore, we propose to add a plugin-function module to preprocess
the input audio before it is fed into the verification system. As a first
attempt, we study two opposite plugin functions: denoising that attempts to
remove or reduce perturbations and noise-adding that adds small Gaussian noises
to an input audio. We show through experiments that both methods can
significantly degrade the performance of a state-of-the-art adversarial attack.
Specifically, it is shown that denoising and noise-adding can reduce the
targeted attack success rate of the attack from 100% to only 56% and 5.2%,
respectively. Moreover, noise-adding can slow down the attack 25 times in speed
and has a minor effect on the normal operations of a speaker verification
system. Therefore, we believe that noise-adding can be applied to any speaker
verification system against adversarial attacks. To the best of our knowledge,
this is the first attempt in applying the noise-adding method to defend against
adversarial attacks in speaker verification systems.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15046503 |
| Date | 26 July 2021 |
| Creators | Li-Chi Chang (11178210) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Defending_against_Adversarial_Attacks_in_Speaker_Verification_Systems/15046503 |
Page generated in 0.0023 seconds