42 citations · 207 across the 76 of their papers we have counts for
7 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…
On a caching system with object sharing
George Kesidis, Nader Alfares, Xi Li +3
We consider a content-caching system thatis shared by a number of proxies. The cache could belocated in an edge-cloud datacenter and the proxies couldeach serve a large population…
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…
The distribution of age-of-information performance measures for message processing systems
George Kesidis, Takis Konstantopoulos, Michael Zazanis
The idea behind the recently introduced "age of information" performance measure of a networked message processing system is that it indicates our knowledge regarding the "freshnes…
Overbooking Microservices in the Cloud
George Kesidis
We consider the problem of scheduling serverless-computing instances such as Amazon Lambda functions, or scheduling microservices within (privately held) virtual machines (VMs). In…