23 citations · 60 across the 17 of their papers we have counts for
17 papers
Backdoor Mitigation by Correcting the Distribution of Neural Activations
Xi Li, Zhen Xiang, David J. Miller +1
Backdoor (Trojan) attacks are an important type of adversarial exploit against deep neural networks (DNNs), wherein a test instance is (mis)classified to the attacker's target clas…
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…
UMD: Unsupervised Model Detection for X2X Backdoor Attacks
Zhen Xiang, Zidi Xiong, Bo Li
Backdoor (Trojan) attack is a common threat to deep neural networks, where samples from one or more source classes embedded with a backdoor trigger will be misclassified to adversa…
Training set cleansing of backdoor poisoning by self-supervised representation learning
H. Wang, S. Karami, O. Dia +6
A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small numbe…
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…
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Zhen Xiang, David J. Miller, George Kesidis
Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. A victim classifier will predict to an attacker-desired target class whenever a test sample is emb…
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks
Xi Li, Zhen Xiang, David J. Miller +1
Backdoor (Trojan) attacks are emerging threats against deep neural networks (DNN). A DNN being attacked will predict to an attacker-desired target class whenever a test sample from…
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 BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers
Xi Li, David J. Miller, Zhen Xiang +1
Data Poisoning (DP) is an effective attack that causes trained classifiers to misclassify their inputs. DP attacks significantly degrade a classifier's accuracy by covertly injecti…
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…
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…
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters
David J. Miller, Xinyi Hu, Zhen Xiang +1
Naive Bayes spam filters are highly susceptible to data poisoning attacks. Here, known spam sources/blacklisted IPs exploit the fact that their received emails will be treated as (…