University of Waterloo (UW) cybersecurity PhD student Andre Kassis published his findings after being granted access to an account protected with biometrics using deepfake AI-generated audio recordings.
A hacker can create a deepfake voice with five minutes of the target’s recorded voice, which can be taken from public posts on social media, the research shows. GitHub’s open source AI software can create deepfake audio that can surpass voice authentication.
He used the deepfake to expose a weakness in the Amazon Connect voice authentication system, a UW release reveals. Four-second attacks on Connect had a 10 percent success rate, and attacks closer to 30 seconds were successful 40 percent of the time.
In response, the company added biometric anti-spoofing software that could find digital markers on a voice recording, revealing if it was made by a machine or human. This worked until Kassis used free software to remove the digital markers from his deepfakes.
Read more: PhD Student Uses Deepfake to Pass Popular Voice Authentication and Spoof Detection System
