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 …

Since the emergence of security concerns in artificial intelligence (AI), there has been
significant attention devoted to the examination of backdoor attacks. Attackers can utilize …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the
rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets …

We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on
Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a …

Textual backdoor attacks are a kind of practical threat to NLP systems. By injecting a
backdoor in the training phase, the adversary could control model predictions via predefined …

Recent studies demonstrated that deep neural networks (DNNs) are vulnerable to backdoor
attacks. The attacked model behaves normally on benign samples, while its predictions are …

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

Backdoor attacks, which maliciously control a well-trained model's outputs of the instances
with specific triggers, are recently shown to be serious threats to the safety of reusing deep …

A new collaborative learning, called split learning, was recently introduced, aiming to protect
user data privacy without revealing raw input data to a server. It collaboratively runs a deep …