Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

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

Public resources and services (eg, datasets, training platforms, pre-trained models) have
been widely adopted to ease the development of Deep Learning-based applications …

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 …

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger
samples during the inference stage, ie, the test-time trigger sample detection, can prevent …

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, which poison the training
set to alter the model prediction over samples with a specific trigger. While existing efforts …

As an effective way to acquire accurate information about the driving environment, LiDAR
perception has been widely adopted in autonomous driving. The state-of-the-art LiDAR …

Deep learning has significantly transformed face recognition, enabling the deployment of
large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep …

Backdoor attacks against deep learning have been widely studied. Various attack
techniques have been proposed for different domains and paradigms, eg, image, point …