Citation: | ZHANG Xuejun, BAO Junda, HE Fucun, et al. A fingerprint indoor localization method against adversarial sample attacks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2087-2101. doi: 10.13700/j.bh.1001-5965.2021.0789(in Chinese) |
With the development of urban intelligence, the indoor positioning services based on WiFi received signal strength (RSS) have attracted extensive attention of society. The deep learning technology is a powerful method to achieve high indoor positioning performance using RSS signal. However, it is vulnerable to adversarial sample attack, which brings serious security risks to the indoor positioning system. In this paper, we propose a deep learning based fingerprint indoor localization method using WiFi RSS against adversarial samples attack (AdvILoc), leveraging the research and analysis of anti-sample defense methods in the field of image recognition. The AdvILoc defend against adversarial samples attack through adding a polling layer, a full connection layer, and a noise layer with differential privacy to the fingerprint indoor positioning deep learning model, which contemplates the characteristics of single and dimension of RSS signals. It also solves the problem of overfitting and weak generalization of deep learning based fingerprint indoor localization model. Meanwhile, the robustness of the model against adversarial samples attack is improved by adding a Dropout layer and designing the parameters regularization of model. The experimental results on two real indoor RSS fingerprint datasets show that, compared with the existing indoor localization methods based on multi-layer perception (MLP) and convolution neural network (CNN), the AdvILoc improves the robustness of the localization model against adversarial samples attack without compromising the localization performance. Additionally, under the C&W attack that meets the
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