The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

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 …

Though deep neural networks (DNNs) have demonstrated excellent performance in
computer vision, they are susceptible and vulnerable to carefully crafted adversarial …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Self-supervised learning in computer vision trains on unlabeled data, such as images or
(image, text) pairs, to obtain an image encoder that learns high-quality embeddings for input …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Patch attacks, one of the most threatening forms of physical attack in adversarial examples,
can lead networks to induce misclassification by modifying pixels arbitrarily in a continuous …

Object detection plays a key role in many security-critical systems. Adversarial patch attacks,
which are easy to implement in the physical world, pose a serious threat to state-of-the-art …

Abstract Model quantization is widely used to compress and accelerate deep neural
networks. However recent studies have revealed the feasibility of weaponizing model …