2 citations · 3 across the 5 of their papers we have counts for
8 papers
Versailles-FP dataset: Wall Detection in Ancient
Wassim Swaileh, Dimitrios Kotzinos, Suman Ghosh +3
Access to historical monuments' floor plans over a time period is necessary to understand the architectural evolution and history. Such knowledge bases also helps to rebuild the hi…
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…
Person Re-identification based on Robust Features in Open-world
Yaguan Qian, Anlin Sun
Deep learning technology promotes the rapid development of person re-identifica-tion (re-ID). However, some challenges are still existing in the open-world. First, the existing re-…
Visually Imperceptible Adversarial Patch Attacks on Digital Images
Yaguan Qian, Jiamin Wang, Bin Wang +4
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted more attention. Many algorithms have been proposed to craft powerful adversarial examples. Ho…
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
Yaguan Qian, Qiqi Shao, Jiamin Wang +5
With the boom of edge intelligence, its vulnerability to adversarial attacks becomes an urgent problem. The so-called adversarial example can fool a deep learning model on the edge…
TEAM: We Need More Powerful Adversarial Examples for DNNs
Yaguan Qian, Ximin Zhang, Bin Wang +4
Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassificati…