Advanced System for Secure Military Communication Using Image Steganography and Block-Rotary Encryption
Year: 2025
Type: article
Abstract: This project offers a thorough security framework for safe military data transfer, including face recognition, block-rotary encryption, and image steganography to protect private data. Block-rotary encryption further secures the buried data by jumbling it with a secret key, while picture steganography hides data within image pixels, rendering it invisible to the human eye. Face recognition is used for multi-factor authentication to improve security, requiring soldiers to use facial recognition to confirm their identification. Administrators, Departments, and Soldiers are among the user roles that the system offers; each has specific access requirements and features. While departments supervise soldiers and decode vital information, administrators control accounts and deal with data decryption. Sensitive information can be safely exchanged and encoded by soldiers. The system offers a strong solution for secret and safe military communication by fusing cutting-edge encryption techniques with role-based access control, biometric authentication, and data secrecy. The suggested solution responds to the increasing demand for secure communication in military operations by providing a tiered security strategy. It guarantees that sensitive data is hidden and shielded from unwanted access during transmission by fusing steganography, encryption, and biometric authentication. While block-rotary encryption adds another layer of complexity that prevents decryption without the right key, image steganography enables the secret embedding of data within common images. By ensuring that various users, including administrators, departments, and soldiers, have the proper access privileges according to their duties, role-based access management reduces the possibility of data breaches. All things consider, this system offers a very safe, comprehensive way to protect military communications from possible dangers.
Authors: Malavika V, Vinish A -, Dr. Shaji B -, Dr. Justin Jose -
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408886342