Enhanced Color Image Encryption Utilizing a Novel Vigenere Method with Pseudorandom Affine Functions
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2024, Vol 3, Issue 1
Abstract
In the realm of digital image security, this study presents an innovative encryption methodology for color images, significantly advancing the traditional Vigenere cipher through the integration of two extensive pseudorandom substitution matrices. These matrices are derived from chaotic maps widely recognized for their cryptographic utility, specifically the logistic map and the skew tent map, chosen for their straightforward implementation capabilities in encryption systems and their high sensitivity to initial conditions. The process commences with the vectorization of the original image and the computation of initial values to alter the starting pixel's value, thereby initiating the encryption sequence. A novel aspect of this method is the introduction of a Vigenere mechanism that employs dynamic pseudorandom affine functions at the pixel level, enhancing the cipher's robustness. Subsequently, a comprehensive permutation strategy is applied to bolster the vector's integrity and elevate the temporal complexity against potential cryptographic attacks. Through simulations conducted on a varied collection of images, encompassing different sizes and formats, the proposed encryption technique demonstrates formidable resilience against both brute-force and differential statistical attacks, thereby affirming its efficacy and security in safeguarding digital imagery.
Authors and Affiliations
Hamid El Bourakkadi, Abdelhakim Chemlal, Hassan Tabti, Mourad Kattass, Abdellatif Jarjar, Abdellhamid Benazzi
Information Acquisition Method of Tomato Plug Seedlings Based on Cycle-Consistent Adversarial Network
In order to solve the interference caused by the overlapping and extrusion of adjacent plug seedlings, accurately obtain the information of tomato plug seedlings, and improve the transplanting effect of automatic tomato...
Performance Comparison of Three Classifiers for Fetal Health Classification Based on Cardiotocographic Data
The global child mortality rate, which is steadily declining, will be around 26 fatalities per 1000 live births in 2022. Numerous Sustainable Development Goals of the United Nations take into account the declining child...
Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine
This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid...
Application of Low-Rank Tensor Completion Combined with Prior Knowledge in Visual Data
In recent years, representing computer vision data in tensor form has become an important method of data representation. However, due to the limitations of signal acquisition devices, the actual data obtained may be dama...
Investigating Stance Marking in Computer-Assisted AI Chatbot Discourse
Stance, a critical discourse marker, reflects the expression of attitudes, feelings, evaluations, or judgments by speakers or writers toward a topic or other participants in a conversation. This study investigates the ma...