Deep neural networks (DNNs) have been verified to be easily attacked by well-designed
adversarial perturbations. Image objects with small perturbations that are imperceptible to …

Deep neural network-based image classifications are vulnerable to adversarial
perturbations. The image classifications can be easily fooled by adding artificial small and …

Adversarial training (AT) is one of the most effective defenses against adversarial attacks for
deep learning models. In this work, we advocate incorporating the hypersphere embedding …

Infrared and visible image fusion is a powerful technique that combines complementary
information from different modalities for downstream semantic perception tasks. Existing …

In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …

Monocular Depth Estimation (MDE) is a critical component in applications such as
autonomous driving. There are various attacks against MDE networks. These attacks …

Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous
driving. However, various attacks target MDE models, with physical attacks posing …

Classification has been the focal point of research on adversarial attacks, but only a few
works investigate methods suited to denser prediction tasks, such as semantic …

Image segmentation is a crucial vision task that groups pixels within an image into
semantically meaningful segments, which is pivotal in obtaining a fine-grained …

In light of the significant progress made in the development and application of semantic
segmentation tasks, there has been increasing attention towards improving the robustness …