Identifying Audio Adversarial Examples via Anomalous Pattern Detection
Machine Learning
2020-07-28 v2 Cryptography and Security
Sound
Audio and Speech Processing
Machine Learning
Abstract
Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99.9% similar to a benign sample. Given the wide application of DNN-based audio recognition systems, detecting the presence of adversarial examples is of high practical relevance. By applying anomalous pattern detection techniques in the activation space of these models, we show that 2 of the recent and current state-of-the-art adversarial attacks on audio processing systems systematically lead to higher-than-expected activation at some subset of nodes and we can detect these with up to an AUC of 0.98 with no degradation in performance on benign samples.
Cite
@article{arxiv.2002.05463,
title = {Identifying Audio Adversarial Examples via Anomalous Pattern Detection},
author = {Victor Akinwande and Celia Cintas and Skyler Speakman and Srihari Sridharan},
journal= {arXiv preprint arXiv:2002.05463},
year = {2020}
}