Document Type
Article
Source Publication Title
PETRA 2021
Abstract
We develop deep learning-based classifiers for Audio Event Detection (AED), attacking them next with some white noise disturbances. We show that an attacker can use such simple disturbances to potentially fully avoid detection by AED systems. Prior work has shown that attackers can mislead image classification tasks, however this work focuses on attacks against AED systems, by tampering the audio and not image. This work brings awareness to the designers and manufacturers of AED systems and devices, as these solutions are becoming more ubiquitous by the day.
Publication Date
7-2-2021
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Santos, Rodrigo dos; Kassetty, Ashwitha; and Nilizadeh, Shirin, "Attacking Audio Event Detection Deep Learning Classifiers with White Noise" (2021). Association of Computing Machinery Open Access Agreement Publications. 10.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/10