2 citations · 5 across the 14 of their papers we have counts for
8 papers · 1 filter
DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization
Xiaoqun Liu, Weiming Qi, Qiben Yan
Large language models (LLMs) remain vulnerable to jailbreak attacks in which adversarial prompts induce harmful outputs. Existing defenses often require access to the model interna…
Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving
Ce Zhou, Qiben Yan, Daniel Kent +4
Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a sing…
Decoding Emotions: Unveiling Facial Expressions through Acoustic Sensing with Contrastive Attention
Guangjing Wang, Juexing Wang, Ce Zhou +4
Expression recognition holds great promise for applications such as content recommendation and mental healthcare by accurately detecting users' emotional states. Traditional method…
Transient Adversarial 3D Projection Attacks on Object Detection in Autonomous Driving
Ce Zhou, Qiben Yan, Sijia Liu
Object detection is a crucial task in autonomous driving. While existing research has proposed various attacks on object detection, such as those using adversarial patches or stick…
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation
Ce Zhou, Qiben Yan, Daniel Kent +3
Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a single-camera image to determine the depth of objects, facilita…
Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation
Guangjing Wang, Hanqing Guo, Yuanda Wang +3
Smartphones and wearable devices have been integrated into our daily lives, offering personalized services. However, many apps become overprivileged as their collected sensing data…
The Dark Side of Human Feedback: Poisoning Large Language Models via User Inputs
Bocheng Chen, Hanqing Guo, Guangjing Wang +2
Large Language Models (LLMs) have demonstrated great capabilities in natural language understanding and generation, largely attributed to the intricate alignment process using huma…
A Practical Survey on Emerging Threats from AI-driven Voice Attacks: How Vulnerable are Commercial Voice Control Systems?
Yuanda Wang, Qiben Yan, Nikolay Ivanov +1
The emergence of Artificial Intelligence (AI)-driven audio attacks has revealed new security vulnerabilities in voice control systems. While researchers have introduced a multitude…