177 citations · 611 across the 18 of their papers we have counts for
8 papers · 1 filter
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
Shihao Zhao, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are increasingly deployed in different applications to achieve state-of-the-art performance. However, they are often applied as a black box with limited…
Short-Term and Long-Term Context Aggregation Network for Video Inpainting
Ang Li, Shanshan Zhao, Xingjun Ma +5
Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccur…
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu, Xingjun Ma, James Bailey +1
Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at training time. A backdoor attack installs a backdoor into the victim model by injecting a back…
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Ranjie Duan, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and impe…
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor triggers in DNNs by poisoning training data. A backdoored model behaves normally on clean tes…
Generative Image Inpainting with Submanifold Alignment
Ang Li, Jianzhong Qi, Rui Zhang +2
Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based generat…