The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

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 against …

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 …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Backdoor attack is a common threat to deep neural networks. During testing, samples
embedded with a backdoor trigger will be misclassified as an adversarial target by a …

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 source …

Backdoor data poisoning (aka Trojan attack) is an emerging form of adversarial attack
usually against deep neural network image classifiers. The attacker poisons the training set …

A Backdoor attack (BA) is an important type of adversarial attack against deep neural
network classifiers, wherein test samples from one or more source classes will be (mis) …

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 …

Backdoor data poisoning attacks add mislabeled examples to the training set, with an
embedded backdoor pattern, so that the classifier learns to classify to a target class …