Voice assistants (VA) have become prevalent on a wide range of personal devices such as
smartphones and smart speakers. As companies build voice assistants with extra …

Recent studies have highlighted adversarial examples as a ubiquitous threat to different
neural network models and many downstream applications. Nonetheless, as unique data …

Personal voice assistants (PVAs) are increasingly used as interfaces to digital environments.
Voice commands are used to interact with phones, smart homes, or cars. In the United …

Despite their immense popularity, deep learning-based acoustic systems are inherently
vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems …

Great progress has been made in deep learning over the past few years, which drives the
deployment of deep learning–based applications into cyber-physical systems. But the lack of …

In this work, we demonstrate the existence of universal adversarial audio perturbations that
cause mis-transcription of audio signals by automatic speech recognition (ASR) systems …

Cyber-security issues on adversarial attacks are actively studied in the field of computer
vision with the camera as the main sensor source to obtain the input image or video data …

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 …

Abstract Automatic Speaker Verification (ASV) enables high-security applications like user
authentication or criminal investigation. However, ASV can be subjected to malicious …

Recent studies have highlighted adversarial examples as ubiquitous threats to the deep
neural network (DNN) based speech recognition systems. In this work, we present a U-Net …