Because" out-of-the-box" large language models are capable of generating a great deal of
objectionable content, recent work has focused on aligning these models in an attempt to …

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains,
enabling the resolution of complex tasks spanning image recognition, natural language …

Current adversarial attacks against speaker recognition systems (SRSs) require either white-
box access or heavy black-box queries to the target SRS, thus still falling behind practical …

Like many other tasks involving neural networks, Speech Recognition models are
vulnerable to adversarial attacks. However recent research has pointed out differences …

Abstract The task of No-Reference Image Quality Assessment (NR-IQA) is to estimate the
quality score of an input image without additional information. NR-IQA models play a crucial …

Although the attack rate and the imperceptibility of perturbations are two main concerns of
adversarial attacks, the transferability of adversarial examples is an emerging topic due to …

Speaker recognition system (SRS) serves as the gatekeeper for secure access, using the
unique vocal characteristics of individuals for identification and verification. SRS can be …

Automatic Speech Recognition (ASR) systems take audio signals as inputs and output the
corresponding text transcriptions. The text is then used to execute commands and perform …

Although deep neural networks have demonstrated state-of-the-art performance in several
tasks such as speaker recognition among others, they are highly vulnerable to adversarial …

IoT devices (eg, voice assistants) that execute real-time speech commands are proliferating
fast in our daily lives. In such a device, detecting the correct keyword spoken as a command …