Adversarial attacks and defenses in physiological computing: a systematic review

Abstract

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.

Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition

“…Nowadays, adversarial attack already poses a security risk for brain-computer interface applications. For example, in BCI-based driver drowsiness estimation, adversarial attacks may make a drowsy driver look alert, increasing the risk of accidents (Wu et al 2021). Recent studies further show that adversarial attacks are more severe on imbalanced datasets (Possas and Zhou 2017), which is the case encountered in EEG-based emotion recognition, inspiring us to pay attention to prevent potential security problems.…”

Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

“…Most prior research on EEG decoding primarily focused on the accuracy and efficiency of machine learning algorithms [3].…”