# Dynamic structure driven image scrambling technique for data protection

## TL;DR

This paper introduces a new image scrambling method using binary trees and hash tables to enhance image security during transmission.

## Contribution

A novel image scrambling technique based on dynamic data structures like binary trees and hash tables is proposed.

## Key findings

- The proposed method achieves 100% similarity between the original and descrambled images.
- The scrambling method shows near-zero correlation between the original and scrambled images.
- The method maintains high image quality with PSNR of infinity for the descrambled image.

## Abstract

Today, communication using digital media has increased rapidly. In this digital communication era, providing security to sensitive images is essential at the time of transmission. The images are mostly focused nowadays because they are used directly or indirectly in every field of information sharing, for example, healthcare, military, intellectual property, and many more areas. In this paper a new approach to image scrambling is proposed to secure the sensitive image information. The proposed method is focused on the core concept of data structure. It involves the use of binary trees and the efficiency of hash tables, which enhances image security during transmission. The dynamic data structure properties enhanced the scrambling and descrambling process. In the scrambling method, the image pixels are first stored in the binary tree using the hash table. After the binary tree arrangement, pixels are collected into a one-dimensional array using the tree traversing process. Now, the one-dimensional array is converted into the two-dimensional array to match the size of the original image. The descrambling method is the inverse of the scrambling method. The proposed method maintains the quality of the image for both sender and receiver; different quality assessment parameters like PSNR, MSE, NCC, AD, SC, MD, Corr, HC, VC, DC, NPCR, UACI and Entropy are used to check the outcome. The outcome of PSNR between the original image and the scrambled image is less than 4 dB. For the descrambled image and the original image, the PSNR is infinite. According to the obtained results, there is a 100% similarity between the original image and the descrambled image. The proposed method was also compared with the existing methods, and it showed a negative or near to ‘0’ correlation between the scrambled image and the original image. In future work the proposed scrambling method can be used in image watermarking or image steganography techniques.