Privacy-Preserving and Efficient Border Surveillance System using Advanced Deep Learning and Cryptographic Techniques

This research presents a comprehensive review of a novel border surveillance system that leverages advanced deep learning and cryptographic techniques. This study reviews the surveillance system that integrates MobileNet for real-time object detection, which employs robust encryption methods, and utilizes a scalable and efficient system architecture. By dynamically adjusting security protocols based on real-time threats, the system enhances resilience and privacy. The societal benefits of this border surveillance system include the protection of critical infrastructure and improved national security. This review provides valuable insights into the latest advancements in border surveillance technology and highlights the potential of deep learning and cryptography in enhancing security measures.