6 citations · 17 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Robust and Active Learning for Deep Neural Network Regression
Xi Li, George Kesidis, David J. Miller +3
We describe a gradient-based method to discover local error maximizers of a deep neural network (DNN) used for regression, assuming the availability of an "oracle" capable of provi…
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…