Adversarial attacks, which attract explosive rese-arch attention in recent years, have
achieved fantastic success in fooling neural networks, especially for image-classification …

With the widespread use of automated speech recognition (ASR) systems in modern
consumer devices, attack against ASR systems have become an attractive topic in recent …

Prior researchers show that existing automatic speech recognition (ASR) systems are
vulnerable to adversarial examples. Most existing adversarial attacks against ASR systems …

Machine Learning systems are vulnerable to adversarial attacks and will highly likely
produce incorrect outputs under these attacks. There are white-box and black-box attacks …

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 …

Speech is a common and effective way of communication between humans, and modern
consumer devices such as smartphones and home hubs are equipped with deep learning …

Recently studies show that adversarial examples (AEs) can pose a serious threat to a “white-
box” automatic speech recognition (ASR) system, when its machine-learning model is …

Adversarial attacks against commercial black-box speech platforms, including cloud speech
APIs and voice control devices, have received little attention until recent years. Constructing …

Like many other tasks involving neural networks, Speech Recognition models are
vulnerable to adversarial attacks. However recent research has pointed out differences …

The ubiquitous presence of machine learning systems in our lives necessitates research into
their vulnerabilities and appropriate countermeasures. In particular, we investigate the …