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

As governments and corporations adopt deep learning systems to collect and analyze user-
generated audio data, concerns about security and privacy naturally emerge in areas such …

Ubiquitous microphones on smart devices considerably raise users' concerns about speech
privacy. Since the microphones are primarily controlled by hardware/software developers …

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial
examples, raising broad security concerns about their applications. Besides the attacks in …

The integration of deep learning on Speaker Recognition (SR) advances its development
and wide deployment, but also introduces the emerging threat of adversarial examples …

The emergence of Artificial Intelligence (AI)-driven audio attacks has revealed new security
vulnerabilities in voice control systems. While researchers have introduced a multitude of …

In light of the widespread application of Automatic Speech Recognition (ASR) systems, their
security concerns have received much more attention than ever before, primarily due to the …

Physical adversarial examples (PAEs) are regarded as" whistle-blowers" of real-world risks
in deep-learning applications. However, current PAE generation studies show limited …

A wide variety of adversarial attacks have been proposed and explored using image and
audio data. These attacks are notoriously easy to generate digitally when the attacker can …

In this thesis, we tackle key challenges in processing audio signals, specifically focusing on
speech and music. These signals are crucial for human interaction with both the …