Modified Snake Optimization Based Robust Steganography with Chaotic Encryption Scheme for Remote Sensing Imagery

• Published in: 2023 6th International Conference on Engineering Technology and its Applications (IICETA) pp. 641 - 647
• Main Authors: Al-luhiby, Hussein A., Alkhafaij, Mahdi abdulkhudur, Ali, Eyhab, Saleem, Munqith, Ali, Rabei Raad, Habelalmateen, Mohammed I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15-07-2023
• Subjects: Chaotic communication; Cybersecurity; Encryption; Image steganography; Information leakage; Modified snake optimization algorithm; Receivers; Remote sensing; Simulation; Steganography; Urban planning
• ISSN: 2831-753X
• DOI: 10.1109/IICETA57613.2023.10351456

High-resolution remote sensing images (RSI) have recently been broadly implemented in several domains like urban planning, agriculture, and forestry. A high-resolution RSI can illustrate the texture characteristics of ground details very clearly; however, it even provides new difficulties to registration like image information leakage, texture similarity, and image storage space. Steganography can be utilized to hide data for security and provide an identification mark. One of the techniques to conceal secret messages is steganography which is safe and tough to find through a naked eye. This paper presents a Modified Snake Optimization based Robust Steganography with Chaotic Encryption Scheme (MSORS-CES) for RSI. The presented MSORS-CES technique aims to obtain secure transmission of remote sensing images. Initially, the chaotic encryption technique is applied for the encryption of secret images. At the same time, the MSO algorithm is used for optimal pixel selection (OPS) process. As well, the encrypted secret image can be embedded into the chosen pixel values in cover image. At the receiver end, the extraction and image decryption processes were performed. The experimental validation of the MSORS-CES technique is examined on a set of remote sensing images and the results are well studied. The simulation results show the improved performance of the MSORS-CES technique over other models.