High Capacity Steganographic Scheme for JPEG Compression Using Particle Swarm Optimization

2012 ◽  
Vol 433-440 ◽  
pp. 5118-5122
Author(s):  
Feno Heriniaina Rabevohitra ◽  
Jun Sang
Keyword(s):  
Particle Swarm Optimization ◽  
High Capacity ◽  
Particle Swarm ◽  
Jpeg Compression ◽  
Substitution Matrix ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Best Fit

A steganographic scheme for JPEG compressed image with high capacity and with good quality of the stego-image was presented. A quantization table of size 16*16 was used instead of the commonly used size 8*8 in most JPEG compression to obtain higher embedding capacity. In addition, to improve the quality of the stego-image, particle swarm optimization (PSO) was applied to find an optimal substitution matrix to transform the secret data into the best fit for the cover image before embedding. The experimental results show that, for the proposed scheme, the improvement of the quality of the stego-image and a higher capacity of the secret data was achieved.

scholarly journals A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Aya Jaradat ◽  
Eyad Taqieddin ◽  
Moad Mowafi
Keyword(s):  
Particle Swarm Optimization ◽  
Image Quality ◽  
High Capacity ◽  
Particle Swarm ◽  
Image Steganography ◽  
Embedding Capacity ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization

Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.

scholarly journals Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

Open Physics ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 452-462 ◽  
Author(s):  
Duraisamy Jude Hemanth ◽  
Subramaniyan Umamaheswari ◽  
Daniela Elena Popescu ◽  
Antoanela Naaji
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Image Steganography ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Transform Coefficients

AbstractImage steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

scholarly journals Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hai Zhu ◽  
Xingsi Xue ◽  
Aifeng Geng ◽  
He Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Location Based Services ◽  
Sensor Data ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Swarm Optimization

In recent years, innovative positioning and mobile communication techniques have been developing to achieve Location-Based Services (LBSs). With the help of sensors, LBS is able to detect and sense the information from the outside world to provide location-related services. To implement the intelligent LBS, it is necessary to develop the Semantic Sensor Web (SSW), which makes use of the sensor ontologies to implement the sensor data interoperability, information sharing, and knowledge fusion among intelligence systems. Due to the subjectivity of sensor ontology engineers, the heterogeneity problem is introduced, which hampers the communications among these sensor ontologies. To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms. Among all ontology matching technologies, Particle Swarm Optimization (PSO) can represent a contributing method to deal with the low-quality ontology alignment problem. For the purpose of further enhancing the quality of matching results, in our work, sensor ontology matching is modeled as the meta-matching problem firstly, and then based on this model, aiming at various similarity measures, a Simulated Annealing PSO (SAPSO) is proposed to optimize their aggregation weights and the threshold. In particular, the approximate evaluation metrics for evaluating quality of alignment without reference are proposed, and a Simulated Annealing (SA) strategy is applied to PSO’s evolving process, which is able to help the algorithm avoid the local optima and enhance the quality of solution. The well-known Ontology Alignment Evaluation Initiative’s benchmark (OAEI’s benchmark) and three real sensor ontologies are used to verify the effectiveness of SAPSO. The experimental results show that SAPSO is able to effectively match the sensor ontologies.

scholarly journals Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm

2020 ◽  
Author(s):  
Mohammed Ajuji ◽  
Aliyu Abubakar ◽  
Datti, Useni Emmanuel
Keyword(s):  
Particle Swarm Optimization ◽  
Time Complexity ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Worst Case ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Nature Inspired Algorithms

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

scholarly journals Evaluation of cloud computing resource scheduling based on improved optimization algorithm

2020 ◽  
Author(s):  
Huafeng Yu
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Operation Time ◽  
Swarm Optimization ◽  
Ipso Algorithm

Abstract Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

A Modified Particle Swarm Optimization Algorithm with Cases Studies

2011 ◽  
Vol 268-270 ◽  
pp. 823-828
Author(s):  
Cheng Chien Kuo ◽  
Hung Cheng Chen ◽  
Teng Fa Taso ◽  
Chin Ming Chiang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Fast Calculation ◽  
Modified Particle Swarm Optimization ◽  
Stochastic Optimization Algorithms

s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made from different algorithms are then compared between the quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the simulation results, SA-PSO obtained higher efficiency, better quality and faster convergence speed than other compared algorithms.

scholarly journals High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

2018 ◽  
Vol 8 (11) ◽  
pp. 2199 ◽  
Author(s):  
Abdul Zakaria ◽  
Mehdi Hussain ◽  
Ainuddin Wahab ◽  
Mohd Idris ◽  
Norli Abdullah ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
High Capacity ◽  
Visual Quality ◽  
Data Mapping ◽  
Embedding Capacity ◽  
Secret Data ◽  
Stego Image ◽  
Mapping Strategy ◽  
Lsb Substitution

Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.

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