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 …

This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …

Backdoor attack has emerged as a major security threat to deep neural networks (DNNs).
While existing defense methods have demonstrated promising results on detecting or …

Recently, machine learning models have demonstrated to be vulnerable to backdoor
attacks, primarily due to the lack of transparency in black-box models such as deep neural …

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 shown that DNNs can be compromised by backdoor attacks crafted at
training time. A backdoor attack installs a backdoor into the victim model by injecting a …

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time
attack that injects a trigger pattern into a small proportion of training data so as to control the …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Recent studies have shown that deep neural networks (DNN) are vulnerable to various
adversarial attacks. In particular, an adversary can inject a stealthy backdoor into a model …

We investigate a new method for injecting backdoors into machine learning models, based
on compromising the loss-value computation in the model-training code. We use it to …