Emerging AI Security Threats for Autonomous Cars -- Case Studies
원문 초록
Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various attacks. Adversaries can launch model extraction attacks for monetization purposes or step-ping-stone towards other attacks like model evasion. In specific cases, it even results in destroying brand reputation, differentiation, and value proposition. In addition, IP laws and AI-related legalities are still evolving and are not uniform across countries. We discuss model extraction attacks in detail with two use-cases and a generic kill-chain that can compromise autonomous cars. It is essential to investigate strategies to manage and mitigate the risk of model theft.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
AdvReal: Adversarial Patch Generation Framework with Application to Adversarial Safety Evaluation of Object Detection Systems
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios
Task-Driven Implicit Representations for Automated Design of LiDAR Systems
Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
토론
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