Abstract In recent years, Deep Neural Networks (DNN) have emerged as a practical method
for image recognition. The raw data, which contain sensitive information, are generally …

Autonomous driving systems require many images for analyzing the surrounding
environment. However, there is fewer data protection for private information among these …

Image classification is a fundamental task in image processing. Despite the long time
research, there are still many challenging problems to be solved. In this study, we introduce …

This paper presents SimMIM, a simple framework for masked image modeling. We have
simplified recently proposed relevant approaches, without the need for special designs …

Masked image modeling (MIM) has attracted much research attention due to its promising
potential for learning scalable visual representations. In typical approaches, models usually …

Masked image modeling has been demonstrated as a powerful pretext task for generating
robust representations that can be effectively generalized across multiple downstream tasks …

Nowadays sample selection is drawing increasing attention. By extracting and training only
on the most informative subset, sample selection can effectively reduce the training cost …

In this study, a perceptually hidden object-recognition method is investigated to generate
secure images recognizable by humans but not machines. Hence, both the perceptual …

To achieve automatic recommendation of privacy settings for image sharing, a new tool
called iPrivacy (image privacy) is developed for releasing the burden from users on setting …

Deep models are commonly treated as black-boxes and lack interpretability. Here, we
propose a novel approach to interpret deep image classifiers by generating discrete masks …