TransGAN-Based Secure Indoor Localization Against Adversarial Attacks

Received signal strength (RSS)-based WiFi fingerprint localization has attracted much attention for global positioning system denied-areas. Deep neural network (DNN) has introduced innovative techniques for indoor localization. However, deep learning models are susceptible to adversarial attacks, so the performance of indoor positioning methods is seriously threatened by adversarial attacks. To improve the localization performance, we first investigate the impact of adversarial attacks on indoor localization systems. A secure adversarial location guard framework, adv-LG, is then developed. It consists of a transformer-based generative adversarial network (TransGAN) and a cleaner module. TransGAN is developed to learn the mapping from adversarial samples to clean ones, while the cleaner module aims to remove adversarial perturbations from the adversarial samples using the learned mapping. The cleaned data is finally fed into a deep learning model to achieve online localization. We compare the localization performance of the proposed adv-LG method with adversarial training (AT), Gaussian smoothing (GS), and autoencoder (AE)-based approaches on two publicly available datasets, i.e., UJIIndoorLoc and UTSIndoorLoc. The results show that adv-LG exhibits significant advantages in classification and localization performance under several typical adversarial attack scenarios.