Deep learning models are often trained on distributed, webscale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

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 …

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 …

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 attacks aim to cause misclassification of a subject model by stamping a trigger to
inputs. Backdoors could be injected through malicious training and naturally exist. Deriving …

The vulnerability of deep neural networks (DNN) to backdoor (trojan) attacks is extensively
studied for the image domain. In a backdoor attack, a DNN is modified to exhibit expected …

Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs),
such that the prediction of the infected model will be maliciously changed if the hidden …

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 …

The distance between two classes for a deep learning classifier can be measured by the
level of difficulty in flipping all (or majority of) samples in a class to the other. The class …

Deep neural networks have achieved impressive performance in a variety of tasks over the
last decade, such as autonomous driving, face recognition, and medical diagnosis …