Adversarial audio samples generated by AI can trick authentication systems
The researchers trained their system on samples of 107 speakers' utterances to get a better idea of what makes speech sound human. To test their algorithm, they crafted multiple adversarial samples to fool authentication systems – with a 72 percent success rate. Against some fairly weak systems, they achieved a 99 percent success rate after six attempts.
This doesn't mean voice authentication software is defunct just yet, though. Against Amazon Connect – software provided to cloud contact centers – they achieved only ten percent success in a four-second attack, and 40 percent in less than 30 seconds. And authentication software is improving too. Miscreants hoping to carry out these types of attacks need to have access to their target's voice, and be sufficiently tech-savvy enough to generate their own adversarial audio samples if they're trying to crack a more secure system. Although the barrier is high, the researchers warned companies developing voice authentication software to keep working.
"The success rates of our attacks are concerning," they wrote,"primarily due to them being attained in the black-box setting and under the assumptions of realistic threat models." The findings"highlight the severe pitfalls of voice authentication systems and stress the need for more reliable mechanisms." ®
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