Speech and speaker recognition systems are employed in a variety of applications, from
personal assistants to telephony surveillance and biometric authentication. The wide …

Connected and autonomous vehicles (CAVs) will form the backbone of future next-
generation intelligent transportation systems (ITS) providing travel comfort, road safety …

Adversarial examples are inputs to machine learning models designed by an adversary to
cause an incorrect output. So far, adversarial examples have been studied most extensively …

Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where
malicious human-imperceptible perturbations are included in the input to the deep network …

Speaker recognition (SR) is widely used in our daily life as a biometric authentication or
identification mechanism. The popularity of SR brings in serious security concerns, as …

Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

Stealing attack against controlled information, along with the increasing number of
information leakage incidents, has become an emerging cyber security threat in recent …

Existing efforts in audio adversarial attacks only focus on the scenarios where an adversary
has prior knowledge of the entire speech input so as to generate an adversarial example by …

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