Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/513236
Title: | Exploring the Optimized algorithms for enhancing the security And image quality in image steganography |
Researcher: | SMITHA, G L |
Guide(s): | Baburaj, E |
Keywords: | Computer Science Engineering and Technology Imaging Science and Photographic Technology |
University: | Sathyabama Institute of Science and Technology |
Completed Date: | 2020 |
Abstract: | In the modern world of communication, data security plays a newlinevital role as the data is transmitted securely over the internet from one newlineplace to another. Cryptography protects the data from unauthorized newlineusers by applying mathematical formulae and making the information newlineinto some scramble codes. In steganography, the information to be newlinetransferred is concealed into an image in such a manner that the message newlinecan be viewed only by the authorized users. The message is not used by newlineany other party actively or passively. The data communication in the newlineinternet needs new steganographic algorithms to find the optimal newlinelocation in the image where the data can be concealed. The researchers newlineproposed a number of algorithms for the above purpose, but the new newlinealgorithms give promising results. The Sobel operator is incorporated newlinewith Edge Adaptive Least Significant Bit Matching Revisited newline(EALSBMR) which provides fast and reliable results with good newlinecompression ratio as compared with the existing Algorithms. The edges newlineare accurately detected by the Sobel operator that makes the algorithm newlineto perform better. Moreover, different bio inspired algorithms are newlineexperimented to find the optimal solutions, as these algorithms are newlineemerging technologies and based on the principles and inspiration of newlinebiological evolution of nature to design and implement new robust newlinealgorithms. These heuristic algorithms are highly suitable to find the newlineoptimal location of the information to be hidden as the optimality is newlineenhanced in each generation until the exact location is found. By newlineconsidering the quantum of the work the following algorithms are newlinedeployed. The algorithms, Artificial Immune System (AIS), Genetic newlineviii newlineAlgorithm (GA), Particle Swarm Optimization (PSO), Ant Bee Colony newlineOptimization (ABC), and Ant Colony Optimization (ACO) are used for newlinelocating the information into the optimal point. Experimental results newlineshow that PSO and ABC are better algorithms for image steganography newlineas compared with other heuristic algorithms. PSO outperforms than the newlineother approa |
Pagination: | vi, 153 |
URI: | http://hdl.handle.net/10603/513236 |
Appears in Departments: | COMPUTER SCIENCE DEPARTMENT |
Files in This Item:
File | Description | Size | Format | |
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10.annexure.pdf | Attached File | 1.18 MB | Adobe PDF | View/Open |
1.title.pdf | 50.64 kB | Adobe PDF | View/Open | |
2.prelim pages.pdf | 2.24 MB | Adobe PDF | View/Open | |
3.abstract.pdf | 170.12 kB | Adobe PDF | View/Open | |
4.contents.pdf | 337.09 kB | Adobe PDF | View/Open | |
5.chapter 1.pdf | 885.33 kB | Adobe PDF | View/Open | |
6.chapter 2.pdf | 794.7 kB | Adobe PDF | View/Open | |
7.chapter 3.pdf | 943.63 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 50.64 kB | Adobe PDF | View/Open | |
8.chapter 4.pdf | 711.74 kB | Adobe PDF | View/Open | |
9.chapter 5.pdf | 174.07 kB | Adobe PDF | View/Open |
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