105 citations · 298 across the 29 of their papers we have counts for
31 papers
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…
Backdoor Attack and Defense for Deep Regression
Xi Li, George Kesidis, David J. Miller +1
We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples…