6 citations · 6 across the 3 of their papers we have counts for
8 papers
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…
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…
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set
Zhen Xiang, David J. Miller, George Kesidis
Backdoor attacks (BAs) are an emerging form of adversarial attack typically against deep neural network image classifiers. The attacker aims to have the classifier learn to classif…
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing
Zhen Xiang, David J. Miller, George Kesidis
Backdoor data poisoning is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively sma…
Notes on Margin Training and Margin p-Values for Deep Neural Network Classifiers
George Kesidis, David J. Miller, Zhen Xiang
We provide a new local class-purity theorem for Lipschitz continuous DNN classifiers. In addition, we discuss how to achieve classification margin for training samples. Finally, we…
Detection of Backdoors in Trained Classifiers Without Access to the Training Set
Zhen Xiang, David J. Miller, George Kesidis
Recently, a special type of data poisoning (DP) attack targeting Deep Neural Network (DNN) classifiers, known as a backdoor, was proposed. These attacks do not seek to degrade clas…