5 papers
Measuring the Robustness of Audio Deepfake Detection under Real-World Corruption
Xiang Li, Pin-Yu Chen, Wenqi Wei
Deepfakes have emerged as a widespread and rapidly escalating concern in generative AI, spanning images, audio, and videos. Among these, audio deepfakes are particularly alarming d…
Aegis: Towards Governance, Integrity, and Security of AI Voice Agents
Xiang Li, Pin-Yu Chen, Wenqi Wei
With the rapid advancement and adoption of Audio Large Language Models (ALLMs), voice agents are now being deployed in high-stakes domains such as banking, customer service, and IT…
MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
Xiang Li, Bhavani Thuraisingham, Wenqi Wei
Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution…
Where are we in audio deepfake detection? A systematic analysis over generative and detection models
Xiang Li, Pin-Yu Chen, Wenqi Wei
Recent advances in Text-to-Speech (TTS) and Voice-Conversion (VC) using generative Artificial Intelligence (AI) technology have made it possible to generate high-quality and realis…
Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum
Nashrah Haque, Xiang Li, Zhehui Chen +4
We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural net…