6 citations · 8 across the 6 of their papers we have counts for
6 papers
CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
Guangmingmei Yang, Xi Li, Hang Wang +2
A variety of defenses have been proposed against Trojans planted in (backdoor attacks on) deep neural network (DNN) classifiers. Backdoor-agnostic methods seek to reliably detect a…
Post-Training Overfitting Mitigation in DNN Classifiers
Hang Wang, David J. Miller, George Kesidis
Well-known (non-malicious) sources of overfitting in deep neural net (DNN) classifiers include: i) large class imbalances; ii) insufficient training-set diversity; and iii) over-tr…
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection
Hang Wang, Zhen Xiang, David J. Miller +1
Deep neural networks are vulnerable to backdoor attacks (Trojans), where an attacker poisons the training set with backdoor triggers so that the neural network learns to classify t…
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic
Hang Wang, Zhen Xiang, David J. Miller +1
Backdoor attacks are an important type of adversarial threat against deep neural network classifiers, wherein test samples from one or more source classes will be (mis)classified t…
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks
Hang Wang, David J. Miller, George Kesidis
Deep Neural Networks (DNNs) have been shown vulnerable to Test-Time Evasion attacks (TTEs, or adversarial examples), which, by making small changes to the input, alter the DNN's de…
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…