SaTC: CORE: Medium: Securing the Voice Processing Pipeline Against Adversarial Audio

Principal Investigator: Patrick Traynor

Sponsor: National Science Foundation

Start Date: October 1, 2019

End Date: September 30, 2023

Amount: $1,199,996

Abstract

In a world in which many new computing devices have limited or no traditional user interface (e.g., smart thermostats, personal digital assistants including Amazon’s Alexa, etc), voice interfaces are becoming a primary means of interaction. Such systems not only simplify interaction with conventional devices for traditional users, but also promote broader inclusion for both the elderly and those with disabilities. These interfaces have been made significantly more accurate in recent years through the application of deep learning techniques; however, these techniques are subject to a number of attacks using modified audio. While previous researchers have demonstrated such attacks using significant knowledge of specific deep learning models, our initial work demonstrates that knowledge of signal processing (or how voices are turned into the inputs deep learning models require) can create attacks that work across a wide variety of systems. The work proposed in this grant will allow us to fully characterize the security challenges in the space between signal processing and deep learning, and to develop strong defenses to ensure that these systems can continue to operate in the presence of malicious inputs. A wide range of systems, from the Internet of Things (IoT) to infrastructure such as air traffic control, will benefit from improved resilience to malicious audio.

More Information: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933208&HistoricalAwards=false