The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

The Controller Area Network (CAN) is the most widely used in-vehicle communication
protocol, which still lacks the implementation of suitable security mechanisms such as …

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

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 …

With the thriving of deep learning and the widespread practice of using pre-trained networks,
backdoor attacks have become an increasing security threat drawing many research …

In recent years, neural backdoor attack has been considered to be a potential security threat
to deep learning systems. Such systems, while achieving the state-of-the-art performance on …

Diffusion models have achieved great success in a range of tasks, such as image synthesis
and molecule design. As such successes hinge on large-scale training data collected from …

Self-supervised learning in computer vision aims to pre-train an image encoder using a
large amount of unlabeled images or (image, text) pairs. The pre-trained image encoder can …

Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns
(eg, a patch or image transformations) as triggers, which are vulnerable to human …

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 …