6 citations · 22 across the 10 of their papers we have counts for
26 papers
Understanding and Rectifying Safety Perception Distortion in VLMs
Xiaohan Zou, Jian Kang, George Kesidis +1
Recent studies reveal that vision-language models (VLMs) become more susceptible to harmful requests and jailbreak attacks after integrating the vision modality, exhibiting greater…
Detecting Backdoor Attacks Against Point Cloud Classifiers
Zhen Xiang, David J. Miller, Siheng Chen +2
Backdoor attacks (BA) are an emerging threat to deep neural network classifiers. A classifier being attacked will predict to the attacker's target class when a test sample from a s…
Backdoor Attack and Defense for Deep Regression
Xi Li, George Kesidis, David J. Miller +1
We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples…
Robust and Active Learning for Deep Neural Network Regression
Xi Li, George Kesidis, David J. Miller +3
We describe a gradient-based method to discover local error maximizers of a deep neural network (DNN) used for regression, assuming the availability of an "oracle" capable of provi…
Age of Information for Small Buffer Systems
George Kesidis, Takis Konstantopoulos, Michael Zazanis
Consider a message processing system whose objective is to produce the most current information as measured by the quantity known as "age of information". We have argued in previou…
A Backdoor Attack against 3D Point Cloud Classifiers
Zhen Xiang, David J. Miller, Siheng Chen +2
Vulnerability of 3D point cloud (PC) classifiers has become a grave concern due to the popularity of 3D sensors in safety-critical applications. Existing adversarial attacks agains…