K Huang, Y Li, B Wu, Z Qin, K Ren - arXiv preprint arXiv:2202.03423, 2022 - arxiv.org
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 …
K Gao, Y Bai, J Gu, Y Yang… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
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 …
Y Li, Y Li, B Wu, L Li, R He… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
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 …
A Chan, YS Ong - arXiv preprint arXiv:1911.08040, 2019 - arxiv.org
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 …
B Mu, Z Niu, L Wang, X Wang… - Proceedings of the …, 2023 - openaccess.thecvf.com
Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks. In the literature, these two types of attacks are commonly treated as …
N Luo, Y Li, Y Wang, S Wu, Y Tan, Q Zhang - arXiv preprint arXiv …, 2022 - arxiv.org
Backdoor attacks threaten Deep Neural Networks (DNNs). Towards stealthiness, researchers propose clean-label backdoor attacks, which require the adversaries not to alter …