The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

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

Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary
could tamper with a small number of model parameter bits to break the integrity of DNNs. To …

With the popularity of intelligent audio systems in recent years, their vulnerabilities have
become an increasing public concern. Existing studies have designed a set of machine …

Prior researchers show that existing automatic speech recognition (ASR) systems are
vulnerable to adversarial examples. Most existing adversarial attacks against ASR systems …

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 …