Blur the Eyes of UAV: Effective Attacks on UAV-based Infrastructure Inspection
Ashok Vardhan Raja, Laurent Njilla, Jiawei Yuan · 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) · 2021
Unmanned aerial vehicles (UAVs) are increasingly leveraged to perform infrastructure inspection tasks, especially with the support of rapidly evolving AI algorithms and hardware in recent years. While the integration of UAV and AI techniques enhances the efficiency and effectiveness of infrastructure inspection, it also raises security concerns due to the potential vulnerabilities existing in the underlying AI models. In this paper, we propose to investigate and discover these vulnerabilities with the case study on bridge inspection. In particular, we designed a two-stage approach that can construct effective adversarial perturbations that make the UAV miss the detection of risk-prone regions during the inspection. Spatial constraints, physical limits, as well as dynamic environmental changes are taken into consideration in our approach to make it practical in the physical world. We evaluate our approach using the COCO-Bridge dataset. Our experimental results demonstrate the effectiveness of our approach in both white-box attack and black-box attack settings.