A comprehensive security framework for cloud-based remote sensing image storage and retrieval with adversarial attack resistance

Remote sensing and satellite imaging have become essential in various geological and surveillance applications. These systems often rely on cloud platforms for storing satellite and aerial images, introducing trust and security concerns, especially in sensitive domains like border surveillance, monitoring, and reconnaissance. Traditional cloud solutions are prone to data breaches and adversarial attacks, highlighting the need for a secure, end-to-end framework. To address this, we propose a comprehensive security architecture for storing and retrieving sensitive remote sensing images. Our system ensures confidentiality, integrity, and access control, while also resisting adversarial attacks during image retrieval. It adopts a three-phase structure: secure authentication, secure storage, and secure retrieval. Authentication is achieved using a combination of Zero-Knowledge Proof and Quantum Key Distribution, establishing a tamper-proof user verification process. In the storage phase, quantum-based cryptography secures the images, while a deep hashing model resistant to adversarial attacks enables efficient indexing and retrieval. A watermark embedding mechanism helps detect insider threats and support forensic tracking in case of data breaches. Evaluated on a remote sensing image dataset using various backbone networks, our system achieved a retrieval accuracy of 94.77%, outperforming existing models by 8–12%. The framework is well-suited for high-security environments, including military applications.