Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries
embed a hidden backdoor trigger during the training process for malicious prediction …

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 …

Deep neural networks (DNNs) are proved to be vulnerable against backdoor attacks. A
backdoor is often embedded in the target DNNs through injecting a backdoor trigger into …

To mitigate recent insidious backdoor attacks on deep learning models, advances have
been made by the research community. Nonetheless, state-of-the-art defenses are either …

Third-party resources ($ eg $, samples, backbones, and pre-trained models) are usually
involved in the training of deep neural networks (DNNs), which brings backdoor attacks as a …

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 …

Recent works have demonstrated that deep learning models are vulnerable to backdoor
poisoning attacks, where these attacks instill spurious correlations to external trigger …

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 …

Deep learning models are vulnerable to various adversarial manipulations of their training
data, parameters, and input sample. In particular, an adversary can modify the training data …

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 …