Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against
adversarial attacks, raising significant security concerns in deploying SRSs. However, they …

Abstract Machine learning (ML) based classifiers are vulnerable to evasion attacks, as
shown by recent attacks. However, there is a lack of systematic study of evasion attacks on …

Formal verification has emerged as a powerful approach to ensure the safety and reliability
of deep neural networks. However, current verification tools are limited to only a handful of …

Deep learning has become a promising programming paradigm in software development,
owing to its surprising performance in solving many challenging tasks. Deep neural …

As a new programming paradigm, deep learning has expanded its application to many real-
world problems. At the same time, deep learning based software are found to be vulnerable …

Verifying and explaining the behavior of neural networks is becoming increasingly
important, especially when they are deployed in safety-critical applications. In this paper, we …

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge
devices, quantization is widely regarded as one promising technique. It reduces the …

As a new programming paradigm, deep learning (DL) has achieved impressive performance
in areas such as image processing and speech recognition, and has expanded its …

Deep neural networks (DNNs) have achieved remarkable performance in a myriad of
complex tasks. However, lacking of robustness and black-box nature hinder their …

Formal robustness verification of deep neural networks (DNNs) is a promising approach for
achieving a provable reliability guarantee to AI-enabled software systems. Limited scalability …