Work

Escaping Filter-based Adversarial Example Defense: A Reinforcement Learning Approach

Year: 2022

Type: conference-paper

Source: IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS)

DOI: 10.1109/mass56207.2022.00047

Authors: Kaijian Dan, Xinyu Lei, Yantao Li, Huafeng Qin, Shaojiang Deng, Gang Zhou

Institutions: Chongqing University, Michigan Technological University, Chongqing Technology and Business University, William & Mary

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Science Foundation, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: CRII: SaTC: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing, 基于行为特征的移动用户持续认证方法和关键技术研究, G6534755139

OpenAlex ID: W4312505092

FWCI: 0.13

Cites: 47

Cited by: 1

Related to: 10