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 …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

Backdoor attacks introduce manipulated data into a machine learning model's training set,
causing the model to misclassify inputs with a trigger during testing to achieve a desired …

Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor
attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few …

Backdoor defenses have been studied to alleviate the threat of deep neural networks
(DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt …

Recent studies have demonstrated the susceptibility of deep neural networks to backdoor
attacks. Given a backdoored model, its prediction of a poisoned sample with trigger will be …

Backdoor attacks are serious security threats to machine learning models where an
adversary can inject poisoned samples into the training set, causing a backdoored model …

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 to …

Abstract Deep Neural Networks are vulnerable to Trojan (or backdoor) attacks. Reverse-
engineering methods can reconstruct the trigger and thus identify affected models. Existing …