System to Safeguard the Identity of Persons in Photographs through Cryptography and Steganography Techniques Using Chaos

Abstract

Safeguarding the identity of people in photographs or videos published through social networks or television is of great importance to those who do not wish to be recognized. In this paper, a face detecting and coding system is designed with the goal of solving this problem. Mathematical models to generate chaotic orbits are deployed. One of them applies the diffusion technique to scramble the pixels of each face while another implements the confusion technique to alter the relation between plain text and ciphered text. Afterward, another two orbits are utilized for the steganography technique to modify the least significant bit (LSB) to conceal data that would allow authorized users to decipher the faces. To verify the robustness of the proposed encryption algorithm, different tests are performed with the Lena standard image, such as correlation diagrams, histograms, and entropy. In addition, occlusion, noise, and plain image attacks are performed. The results are compared with those of other works, and the proposed system provided high sensitivity at secret key and a large space for the encryption keys, good speed for ciphering, disorder in the cryptogram, security, data integrity, and robustness against different attacks.

Sistema de almacenamiento distribuido para salvaguardar y almacenar información codificada aplicando caos

“…Ramzi Guesmi and M. A. Ben Farah, propusieron un algoritmo para cifrar imágenes médicas donde utilizan Secure Hash Algorithm (SHA-2) y mapas caóticos como: el Logístico, Tent y Sine (Guesmi et al, 2021). También los mapas de Sine y Logístico fueron implementados para aplicar esteganografía y cifrado con la finalidad de codificar los rostros identificados en una fotografía (Flores Siordia et al, 2018). Ping et al desarrollaron un sistema para cifrar imágenes que se basa en autómatas celulares y caos, donde aplican difusión y confusión con un mapa caótico bidimensional 2D LASM (Ping et al, 2018).…”