Service scenarios of advanced secure computer vision applications

Engineering notes

Key topics: autonomous driving, autonomous vehicle, object detection. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Computer vision (CV) is an important branch of artificial intelligence (AI) that allows machines to understand and analyze visual data from images and videos to do things that have traditionally been performed by humans through vision. As these systems become integral to critical domains—including healthcare, surveillance, autonomous driving, and smart cities—the need for robust security and privacy has intensified. This chapter explores the service scenarios of advanced secure computer vision (ASCV), which combines visual data processing with encryption, privacy-preserving AI, and reliability mechanisms. Applications span transportation and autonomous vehicles (e.g., object detection and driver monitoring), surveillance and security systems (e.g., anomaly detection and crowd monitoring), identity and financial services (biometric authentication), etc. The study highlights key security challenges, including adversarial attacks, tampering, system failures, and physical adversarial manipulations, that can deceive CV models—posing risks to safety and trust. Finally, this chapter outlines future directions, emphasizing the need for real-time secure decision-making, explainable and ethical AI, scalable cross-platform solutions, and strong legal frameworks. By integrating technical defenses with ethical safeguards, ASCV can ensure that CV systems remain reliable, privacy-preserving, and trustworthy across diverse applications.

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