Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and p...Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based biometrics.Physiological computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even death.However,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to them.This study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense strategies.We hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.展开更多
基金supported by the Open Research Projects of Zhejiang Lab(2021KE0AB04)the Technology Innovation Project of Hubei Province of China(2019AEA171)+1 种基金the National Social Science Foundation of China(19ZDA104 and 20AZD089)the Independent Innovation Research Fund of Huazhong University of Science and Technology(2020WKZDJC004).Author contributions。
文摘Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based biometrics.Physiological computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even death.However,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to them.This study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense strategies.We hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.
