Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. With the recent uprising of adversarial attack which automatically and strategically look to computing data pertubation in order to fool well-trained classifiers, this project looks into the vulnerability of existing classifiers against adversarial attack and how to improve their resistence and robustness.
1. He Wang, Feixiang He, Zhexi Peng, Tianjia Shao, Yongliang Yang, Kun Zhou, David Hogg, Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack, CVPR 2021
2. Yunfeng Diao, Tianjia Shao, Yongliang Yang, Kun Zhou and He Wang, BASAR:Black-box Attack on Skeletal Action Recognition, CVPR 2021
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 899739 CrowdDNA, EPSRC (EP/R031193/1), NSF China (No. 61772462, No. U1736217), RCUK grant CAMERA (EP/M023281/1, EP/T014865/1) and the 100 Talents Program of Zhejiang University.