B.S. Eleectrical Engineering and Computer Science, 2022
University of Wisconsin - Madison
I am an undergraduate student majoring in Electrical Engineering and Computer Sciences at the University of Wisconsin-Madison. I have a keen interest in the domain of Machine Learning, Signal Processing, Computer Graphics and Security and Privacy.
Recent years have seen a surge of popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial examples. Large number of modern systems protect themselves against such attacks by targeting the artificiality, i.e., they deploy mechanisms to detect the lack of human involvement in generating the adversarial examples. However, these defenses implicitly assume that humans are incapable of producing meaningful and targeted adversarial examples. In this paper, we show that this base assumption is wrong. In particular, we demonstrate that for tasks like speaker identification, a human is capable of producing analog adversarial examples directly with little cost and supervision: by simply speaking through a tube, an adversary reliably impersonates other speakers in eyes of ML models for speaker identification. Our findings extend to a range of other acoustic-biometric tasks such as liveness, bringing into question their use in security-critical settings in real life, such as phone banking.
Find our paper at https://arxiv.org/abs/2202.02751
Pipe Overflow: Smashing Voice Authentication for Fun and Profit
Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz USENIX Winter, 2022
Please contact me via: ywani [at] wisc.edu.
Or add me on LinkedIn here: https://www.linkedin.com/in/yashwani2000/