23 citations · 60 across the 17 of their papers we have counts for
4 papers · 1 filter
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic
Zhen Xiang, David J. Miller, Hang Wang +1
Recently, a backdoor data poisoning attack was proposed, which adds mislabeled examples to the training set, with an embedded backdoor pattern, aiming to have the classifier learn…
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…
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks
David J. Miller, Zhen Xiang, George Kesidis
There is great potential for damage from adversarial learning (AL) attacks on machine-learning based systems. In this paper, we provide a contemporary survey of AL, focused particu…