Research on Intelligent Image Watermarking Schemes Based on Optimization Algorithm

2014 ◽  
Vol 1006-1007 ◽  
pp. 792-796
Author(s):  
Shun Wei Wu
Keyword(s):  
Optimization Algorithm ◽  
Computational Intelligence ◽  
Image Watermarking ◽  
Optimization Algorithms ◽  
Basic Theory ◽  
Digital Products ◽  
Watermark Embedding ◽  
Theory Application ◽  
The One

Watermarking algorithms are the one of the effective methods to protect the copyright of digital products. There are many types of watermarking algorithms, in this paper, methods based on optimization algorithms, also named computational intelligence, are surveyed. The detail procedure of watermark embedding and the watermarking extraction is described, and the basic theory application of computational intelligence is also analyzed. Finally, the advice of these types of watermarking algorithm is given.

Coupling of Optimization Algorithms Based on Swarm Intelligence

2018 ◽  
pp. 27-50 ◽  
Author(s):  
Kamalanand Krishnamurthy ◽  
Mannar Jawahar Ponnuswamy
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Computational Intelligence ◽  
Biomedical Engineering ◽  
Optimization Algorithms ◽  
Bacterial Foraging Optimization ◽  
Complex Problems ◽  
Bacterial Foraging

Swarm intelligence is a branch of computational intelligence where algorithms are developed based on the biological examples of swarming and flocking phenomena of social organisms such as a flock of birds. Such algorithms have been widely utilized for solving computationally complex problems in fields of biomedical engineering and sociology. In this chapter, two different swarm intelligence algorithms, namely the jumping frogs optimization (JFO) and bacterial foraging optimization (BFO), are explained in detail. Further, a synergetic algorithm, namely the coupled bacterial foraging/jumping frogs optimization algorithm (BFJFO), is described and utilized as a tool for control of the heroin epidemic problem.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals Analysis of Risk Factors in Dementia Through Machine Learning

2020 ◽  
pp. 1-17
Author(s):  
Francisco Javier Balea-Fernandez ◽  
Beatriz Martinez-Vega ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
Raquel Leon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optimization Algorithms ◽  
Progressive Increase ◽  
Control Group ◽  
Data Sets ◽  
Modifiable Factors ◽  
Validation Set ◽  
The One ◽  
And Control ◽  
Potential Tool

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

Anti-Print and Scan Halftone Watermarking Algorithm Based on Edge Detection

2013 ◽  
Vol 416-417 ◽  
pp. 1210-1213
Author(s):  
Hua Wen Ai ◽  
Ping Feng Liu ◽  
Sheng Cong Dong
Keyword(s):  
Edge Detection ◽  
Image Watermarking ◽  
Error Diffusion ◽  
Grayscale Image ◽  
Good Resistance ◽  
Visibility Function ◽  
Gray Image ◽  
Halftone Image ◽  
Watermark Embedding ◽  
Edge Points

In order to resist print and scan attacks, a kind of digital halftone image watermarking algorithm is proposed, which is based on the edge detection and improved error diffusion. The edge of gray image is gotten using canny detection. Calculate the noise visibility function values of the edge points. Then, sort the values in ascending order and select the size that equal to the length of watermark as the location of watermark embedding. While the grayscale image turns to halftone image using the improved error diffusion algorithm, binary watermark is embedded in the edge position. Watermark is pretreated with Arnold before embedding to improve the safety of watermark. Experiment results show that the algorithm is good resistance to print and scan attacks, while resistance to shearing, noise and jpeg compression attacks.

scholarly journals Reversible Watermarking in Medical Image Using RDWT and Sub-Sample

Cryptography ◽  
2020 ◽  
pp. 480-497
Author(s):  
Lin Gao ◽  
Tiegang Gao ◽  
Jie Zhao
Keyword(s):  
Medical Image ◽  
Image Watermarking ◽  
Reversible Watermarking ◽  
Encryption Algorithm ◽  
Discrete Wavelet ◽  
Embedding Capacity ◽  
Watermark Embedding ◽  
Watermarking Scheme ◽  
Redundant Discrete Wavelet Transform ◽  
Image Encryption Algorithm

This paper proposed a reversible medical image watermarking scheme using Redundant Discrete Wavelet Transform (RDWT) and sub-sample. To meet the highly demand of the perceptional quality, the proposed scheme embedding the watermark by modifying the RDWT coefficients. The sub-sample scheme is introduced to the proposed scheme for the enhancement of the embedding capacity. Moreover, to meet the need of security, a PWLCM based image encryption algorithm is introduced for encrypting the image after the watermark embedding. The experimental results suggests that the proposed scheme not only meet the highly demand of the perceptional quality, but also have better embedding capacity than former DWT based scheme. Also the encryption scheme could protect the image contents efficiently.

Block Chain Based Robust Image Watermarking Using Edge Detection And Wavelet Transform

2021 ◽  
Author(s):  
Praveen Kumar Mannepalli ◽  
Vineet Richhariya ◽  
Susheel Kumar Gupta ◽  
Piyush Kumar Shukla ◽  
Pushan Kumar Dutta
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Image Watermarking ◽  
Discrete Wavelet ◽  
Imaging Data ◽  
Watermark Embedding ◽  
Hash Algorithm ◽  
Image Protection ◽  
Additional Level ◽  
The Invisible

Abstract Image protection is essential part of the scientific community today. The invisible watermark is widely being used in past to secure the medical imaging data from copyright protection. In this paper novel hybrid combination of the invisible image watermarking and the Blockchain based encryption is proposed to design. The watermarking is implemented using edge detection (ED) of discrete wavelet transform (DWT) coefficient. The medical image is decomposed using L level DWT transform to generate multi-resolution coefficients. The edge detection is applied to HH wavelet band to generate the edge coefficients. To improve robustness difference of dilation and edge coefficient are used for watermark embedding. The watermark image is encrypted using Blockchain based hash algorithm for medical images. Then at the decoding end first decryption is achieved and then image is reconstructed. The results are sequentially presented for both stages. The PSNR performance is compared with additional level of security.

Optimal Image Watermarking using Hybrid Optimization Algorithm

2015 ◽  
Vol 5 (1) ◽  
pp. 27-47
Author(s):  
Satish Ramchandra Todmal ◽  
Suhas Haribhau Patil
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Image Watermarking ◽  
Optimal Location ◽  
Secret Data ◽  
Bee Colony ◽  
Hybrid Optimization Algorithm ◽  
Extraction Algorithm

Image watermarking is a process of embedding secret information into cover image to secure transmission of secret data. Literature presents several image watermarking techniques are based on different transformation such as, wavelet transform, Fourier transform and cosine transform. Most of the authors have been developed an embedding and extraction algorithm newly with the transformed image. Then, the optimal location for embedding the secret data was identified by using optimization algorithm. Accordingly, the authors have developed an optimal robust watermarking technique using genetic algorithm and wavelet transform. In the previous work, watermarks were embedded into the wavelet coefficients of HL and LH band after searching the optimal locations in order to improve both quality of watermarked image and robustness of the watermark. In this work, the authors have developed to improve the genetic algorithm by combining it with Artificial Bee Colony algorithm (ABC Algorithm). Here, they have used hybrid algorithm for finding of optimal location in watermarking process. Finally, the comparative evaluation of the hybrid algorithm will be done with the existing and previous technique using different images and the performance of the extended algorithm will be analyzed using the PSNR, NC with convergence rate.

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