Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks,
where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …

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 …

Deep neural networks (DNNs) are vulnerable to backdoor attack, which does not affect the
network's performance on clean data but would manipulate the network behavior once a …

Deep neural networks (DNNs) are vulnerable to backdoor attacks. Previous works have
shown it extremely challenging to unlearn the undesired backdoor behavior from the …

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 …

Although deep neural networks (DNNs) have made rapid progress in recent years, they are
vulnerable in adversarial environments. A malicious backdoor could be embedded in a …

Recently, backdoor attacks pose a new security threat to the training process of deep neural
networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the …

Deep learning models have recently shown to be vulnerable to backdoor poisoning, an
insidious attack where the victim model predicts clean images correctly but classifies the …

Backdoor attacks threaten Deep Neural Networks (DNNs). Towards stealthiness,
researchers propose clean-label backdoor attacks, which require the adversaries not to alter …

Backdoor attacks are poisoning attacks and serious threats to deep neural networks. When
an adversary mixes poison data into a training dataset, the training dataset is called a …