Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

In this paper, we investigate the adversarial robustness of vision transformers that are
equipped with BERT pretraining (eg, BEiT, MAE). A surprising observation is that MAE has …

Modern deep neural networks (DNNs) are vulnerable to adversarial samples. Sparse
adversarial samples are a special branch of adversarial samples that can fool the target …

A number of image compressive sensing (CS) algorithms were proposed in the past two
decades, aiming at yielding recovered images with the best possible visual effect. However …

The phenomenon that deep neural networks are vulnerable to adversarial examples has
been found for several years. Under the black-box setting, transfer-based methods usually …

Abstract Vision-Language Pre-training (VLP) models have achieved remarkable success in
practice, while easily being misled by adversarial attack. Though harmful, adversarial …

Recently, some approaches are proposed to harness deep convolutional networks to
facilitate superpixel segmentation. The common practice is to first evenly divide the image …

Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the
classifiers by adding small perturbations to the original example. The added perturbations in …

Modern deep neural networks are often vulnerable to adversarial examples. Most exist
attack methods focus on crafting adversarial examples in the digital domain, while only …