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A robust image steganography using teaching learning based optimization based edge detection model for smart cities
Author(s) -
Dhanasekaran K.,
Anandan P.,
Kumaratharan N.
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12348
Subject(s) - computer science , steganography , image (mathematics) , embedding , enhanced data rates for gsm evolution , artificial intelligence , the internet , machine learning , data mining , pattern recognition (psychology) , world wide web
Recently, Internet becomes a most common medium for transferring critical data and the security of the transmitted data gains maximum priority. Image steganography has been developed as a well‐known model of data hiding which verifies the security level of the transferred data. The images offer high capacity, and the occurrence of accessibility over the Internet is more. An effective steganography model is required for achieving better embedding capacity and also maintaining the other variables in an acceptable value. This article introduces a new robust image steganography using Teaching Learning Based Optimization (TLBO) edge detection model. The TBLO is basically a metaheuristic algorithm which is inspired from the teaching and learning procedure in classrooms. The former stage indicates the learning from the teacher and the latter phase represents the interaction among the learners. The experimental validation takes place in a comprehensive way under several views and the outcome pointed out the superior results of the presented model.