Biblio

With the ever-increasing popularity of LiDAR (Light Image Detection and Ranging) sensors, a wide range of applications such as vehicle automation and robot navigation are developed utilizing the 3D LiDAR data. Many of these applications involve remote guidance - either for safety or for the task performance - of these vehicles and robots. Research studies have exposed vulnerabilities of using LiDAR data by considering different security attack scenarios. Considering the security risks associated with the improper behavior of these applications, it has become crucial to authenticate the 3D LiDAR data that highly influence the decision making in such applications. In this paper, we propose a framework, ALERT (Authentication, Localization, and Estimation of Risks and Threats), as a secure layer in the decision support system used in the navigation control of vehicles and robots. To start with, ALERT tamper-proofs 3D LiDAR data by employing an innovative mechanism for creating and extracting a dynamic watermark. Next, when tampering is detected (because of the inability to verify the dynamic watermark), ALERT then carries out cross-modal authentication for localizing the tampered region. Finally, ALERT estimates the level of risk and threat based on the temporal and spatial nature of the attacks on LiDAR data. This estimation of risk and threats can then be incorporated into the decision support system used by ADAS (Advanced Driver Assistance System). We carried out several experiments to evaluate the efficacy of the proposed ALERT for ADAS and the experimental results demonstrate the effectiveness of the proposed approach.

Face is crucial for human identity, while face identification has become crucial to information security. It is important to understand and work with the problems and challenges for all different aspects of facial feature extraction and face identification. In this tutorial, we identify and discuss four research challenges in current Face Detection/Recognition research and related research areas: (1) Unavoidable Facial Feature Alterations, (2) Voluntary Facial Feature Alterations, (3) Uncontrolled Environments, and (4) Accuracy Control on Large-scale Dataset. We also direct several different applications (spin-offs) of facial feature studies in the tutorial.