Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries
embed a hidden backdoor trigger during the training process for malicious prediction …

In recent years, extensive research has been conducted on the vulnerability of ASR systems,
revealing that black-box adversarial example attacks pose significant threats to real-world …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Speaker Verification (SV) is widely deployed in mobile systems to authenticate legitimate
users by using their voice traits. In this work, we propose a backdoor attack MasterKey, to …

Deep learning based voice synthesis technology generates artificial human-like speeches,
which has been used in deepfakes or identity theft attacks. Existing defense mechanisms …

The rapid development of deep neural networks and generative AI has catalyzed growth in
realistic speech synthesis. While this technology has great potential to improve lives, it also …

Recent years have shown substantial interest in revealing vulnerability issues of voice-
controllable systems on smartphones and smart speakers. While significant prior works have …

Automatic Speech Recognition (ASR) systems have improved and eased how humans
interact with devices. ASR system converts an acoustic waveform into the relevant text form …

In this paper, we propose PhantomSound, a query-efficient black-box attack toward voice
assistants. Existing black-box adversarial attacks on voice assistants either apply …

As one of the most representative applications built on deep learning, audio systems,
including keyword spotting, automatic speech recognition, and speaker identification, have …