Research on Image Recognition Technology in the Encryption and Decryption Process of Electronic Archives Driven by Computer Vision

Data security and privacy have become areas of concern as image recognition and computer vision technologies have advanced. In Internet of Things (IoT) systems for remote sensing, conventional encryption methods are computationally challenging and unsuitable, and multimedia data require strong mechanisms. This study proposes a novel Authentication Vision Optimizer (ACVO)-driven module incorporated with advanced cryptographic approaches and computer vision to generate a privacy-preserving image identification system for e-archives in IoT environments. The framework consists of four modules, like encryption module, secure sharing, authentication, and recognition optimization, utilizing high-resolution satellite images captured via electronic remote sensing gathered from the Kaggle. The encryption module employs the Advanced Encryption Standard (AES) algorithm, while the secure sharing module utilizes Visual Cryptography (VC) for human-readable reconstruction. The authentication module uses the Trusted Execution Environments (TEE) approach to ensure data authenticity. The image recognition optimization module utilizes transfer learning to fine-tune pre-trained Efficient Golden Jackal Tuned Deep Convolute Neuronet (EGJ-DCNN) on small-scale encrypted datasets. The study achieved high recognition accuracy on encrypted satellite images utilizing transfer learning optimization, reducing computational time with AES and visual cryptography encryption. Data integrity is maintained optimally in blind watermarking verification, and privacy protection represents high resistance to unofficial access.