Adversarial Black-Box Attacks on Automatic Speech Recognition Systems Using Multi-Objective Evolutionary Optimization
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Adversarial Black-Box Attacks on Automatic Speech Recognition Systems Using Multi-Objective Evolutionary Optimization. / Khare, Shreya; Aralikatte, Rahul; Mani, Senthil.
Proc. Interspeech 2019. International Speech Communication Association (ISCA), 2019. p. 3208-3212.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Adversarial Black-Box Attacks on Automatic Speech Recognition Systems Using Multi-Objective Evolutionary Optimization
AU - Khare, Shreya
AU - Aralikatte, Rahul
AU - Mani, Senthil
PY - 2019/9/15
Y1 - 2019/9/15
N2 - Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems. We apply this framework on two ASR systems: Deepspeech and Kaldi-ASR, which increases the Word Error Rates (WER) of these systems by upto 980%, indicating the potency of our approach. During both un-targeted and targeted attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97 with the original audio.
AB - Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems. We apply this framework on two ASR systems: Deepspeech and Kaldi-ASR, which increases the Word Error Rates (WER) of these systems by upto 980%, indicating the potency of our approach. During both un-targeted and targeted attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97 with the original audio.
U2 - 10.21437/Interspeech.2019-2420
DO - 10.21437/Interspeech.2019-2420
M3 - Article in proceedings
SP - 3208
EP - 3212
BT - Proc. Interspeech 2019
PB - International Speech Communication Association (ISCA)
T2 - Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association
Y2 - 15 September 2019 through 19 September 2019
ER -
ID: 239857996