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 …

Together with impressive advances touching every aspect of our society, AI technology
based on Deep Neural Networks (DNN) is bringing increasing security concerns. While …

As deep neural networks (DNNs) are growing larger, their requirements for computational
resources become huge, which makes outsourcing training more popular. Training in a third …

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 …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

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 …

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 …

Backdoor injection attack is an emerging threat to the security of neural networks, however,
there still exist limited effective defense methods against the attack. In this paper, we …

Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement
learning (RL) systems. However, the existing attacks require the ability to arbitrarily modify …

Although federated learning can provide privacy protection for individual raw data, some
studies have shown that the shared parameters or gradients under federated learning may …