Image Hiding with an Improved Genetic Algorithm and an Optimal Pixel Adjustment Process

2008 ◽  
Author(s):  
Lin-Yu Tseng ◽  
Yung-Kuan Chan ◽  
Yu-An Ho ◽  
Yen-Ping Chu
Keyword(s):  
Genetic Algorithm ◽  
Adjustment Process ◽  
Improved Genetic Algorithm ◽  
Image Hiding

A More Secure Image Hiding Scheme Using Pixel Adjustment and Genetic Algorithm

2013 ◽  
Vol 7 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Omar Banimelhem ◽  
Lo’ai Tawalbeh ◽  
Moad Mowafi ◽  
Mohammed Al-Batati
Keyword(s):  
Genetic Algorithm ◽  
Transformation Method ◽  
Point Of View ◽  
Adjustment Process ◽  
Stego Image ◽  
Secret Image ◽  
Security Issues ◽  
Image Hiding ◽  
The Difference

This paper proposes a more secure image hiding scheme using Optimal Pixel Adjustment Process (OPAP) and Genetic Algorithm (GA). The security issues of key selection that is used in image hiding are addressed. Thus, a more secure scheme is proposed in order to improve the security as well as the quality of the stego-image. Since GA is a semi-blind algorithm, it may select a key that affects the security. Therefore, the authores improve the security by applying image transformation not only using the GA key, but also using a user key. The user key is used to disarrange the pixel locations of the secret image. Then, the GA, using OPAP, selects the key that maximizes the quality as well as the security of the stego-image. From implementation point of view, the scheme uses a simple and fast transformation method that increases the difference between the secret image and its transformed version. The results showed that the resultant disarranged image cannot be detected, and at the same time the stego-image quality is still high.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Combining improved genetic algorithm and matrix semi-tensor product (STP) in color image encryption

2021 ◽  
Vol 183 ◽  
pp. 108041
Author(s):  
Xiuli Chai ◽  
Xiangcheng Zhi ◽  
Zhihua Gan ◽  
Yushu Zhang ◽  
Yiran Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tensor Product ◽  
Image Encryption ◽  
Color Image ◽  
Improved Genetic Algorithm ◽  
Color Image Encryption
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