SAT: A Selective Adversarial Training Approach for WiFi-Based Human Activity Recognition

Recently, the continuous evolution of deep learning has opened up promising avenues to groundbreaking advancements in wireless sensing systems, which significantly enhance the practical applications of WiFi-based Human Activity Recognition (HAR) systems. However, despite these strides, such systems remain susceptible to adversarial attacks. This article unveils the vulnerability of existing WiFi-based HAR systems to common adversaries, revealing their insufficient robustness. While the intuitive approach is to employ adversarial training to fortify the models, our investigation exposes inherent deficiencies in the current approach. Specifically, we confirm that the strength of perturbations directly influences training outcomes. Moreover, even when confined within a specified perturbation radius, the perturbation strength exhibits variability within a prescribed range, potentially giving rise to “extreme” samples that could compromise training results. To address this challenge, we propose a two-stage Selective Adversarial Training (SAT) approach that integrates model confidence calibration and sample selection. Specifically, we start with calibrating the model and then selectively choose samples from all adversarial examples based on the calibrated confidence outputs that align with the desired criteria for adversarial training. This sample-wise perturbation intensity control effectively prevents the inclusion of inappropriate samples in training, a capability lacking in previous domain-wise perturbation control. Our experiments demonstrate that the proposed fine-grained training method, SAT, is both straightforward and effective in augmenting adversarial training results.