Steganography Using Particle Swarm Optimization-A Review. (original) (raw)
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Particle Swarm Optimization (PSO) for Optimization in Video Steganography
Handbook of Research on Natural Computing for Optimization Problems
This chapter describes use of Natural Computing for optimization in the domain of Steganography. Steganography is an art of concealing information in any media. Internet has made digital content popular and very widely used media. Digital contents shared over Internet are text, audio, images and video streams. Video steganography is hiding secret information in video. It is gaining increasing significance as video streams are transmitted frequently over social media sites like Facebook and YouTube. The first part of the chapter introduces steganography and highlights video steganography as an optimization problem. Further in the chapter natural computing is discussed and Particle Swarm Optimization (PSO) a popular natural computing tool is elaborated in details. Experimental results are presented to check the efficacy of the method.
2012
Data hiding is a method of concealing secret data into a cover media and preventing a spectator from being aware of the existence of the hidden message. According to the problems of steganography, the main effort is to provide a better imperceptibility of stego-image that can be done by decreasing distortion of image. One of the popular techniques in data hiding is steganography in which the simple method for image hiding is the Least Significant Bit (LSB) substitution method. Wu et al. proposed two image hiding techniques to improve the quality of the stego-image [1]. The first one is to find the best block matching matrix and the other one is to find the optimal substitution matrices. The proposed method utilizes particle Swarm optimization (PSO) for finding the best pixel locations, and then the secret image is transformed to a new secret image. Optimal Pixel Adjustment (OPA) method is applied to further increase the quality of image. Results are then compared with those obtained...
Open Physics, 2016
Image 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.
Comparison Between PSO and HPSO In Image Steganography
Efficient steganography techniques are needed for the security of digital information over the Internet and for secret data communication. Therefore, many techniques are proposed for steganography. One of these intelligent techniques is Particle Swarm Optimization (PSO) algorithm. Recently, many modifications are made to Standard PSO (SPSO) such as Human-Based Particle Swarm Optimization (HPSO). Therefore, this paper presents image steganography using HPSO in order to find best locations in image cover to hide text secret message. Then, a comparison is done between image steganography using PSO and using HPSO. Experimental results on six (256×256) cover images and different size of secret massages, prove that the performance of the proposed image steganography using HPSO has been improved in comparison with using SPSO.
Hiding Information in Image by Compound Meta-Heuristic Algorithm PSO-SA
International Journal of Computer Science and Artificial Intelligence, 2013
In this study a new method has been presented in order to hide secret information on host image, in a way that the image has been steganographed and has minimum difference to host image and is based on least significant bit (LSB). As security and quality are two main factors in evaluating steganography operation and LSB is a common method and is vulnerable against attacks and noises, researchers have set their priority on finding a substitution matrix in order to improve these two important factors and increase the system's efficiency. In this article, in order to find an optimal substitution matrix, a compound meta-heuristic method has been suggested which uses two optimization algorithms: Particle Swarm Optimization and simulated annealing. Results of simulation with two optimization algorithms PSO and SA and compound meta-heuristic algorithm PSO-SA have been comprised according to PSNR value and show that the stego-image produced by PSO-SA has more quality in comparison with other existing methods.
Steganographic Data Hiding using DWT and Particle Swarm Optimization
International Journal of Computer Applications, 2015
This paper discusses the steganographic data hiding using the wavelet approach and the optimisation technique. The Discrete Wavelet Transform Using Haar Wavelet gives the excellent peak signal to noise ratio (PSNR) and less computation time. The particle swarm optimisation algorithm (PSO) is also used to hide the data so that the PSNR is improved. The results reveal the DWT using Haar wavelet and PSO algorithm gives excellent PSNR.
A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization
Security and Communication Networks, 2021
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.
Information Hiding using LSB Technique based on Developed PSO Algorithm
International Journal of Electrical and Computer Engineering (IJECE), 2018
Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev.-PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most efficient and speed. An agents population is used in determining process of a required goals at search space for solving of problem. The (Dev.-PSO) algorithm is applied to different images; the number of an image which used in the experiments in this paper is three. For all used images, the Peak Signal to Noise Ratio (PSNR) value is computed. Finally, the PSNR value of the stego-A that obtained from blue sub-band colo is equal (44.87) dB, while the stego-B is equal (44.45) dB, and the PSNR value for the stego-C is (43.97)dB, while the vlue of MSE that obtained from the same color sub-bans is (0.00989), stego-B equal to (0.01869), and stego-C is (0.02041). Furthermore, our proposed method has ability to survive the quality for the stego image befor and after hiding stage or under intended attack that used in the existing paper such as Gaussian noise, and salt & pepper noise. Keyword: Cover image Developed PSO algorithm Search space Steganography Stego image
A high_performance steganographic method using JPEG and PSO algorithm
2008 IEEE International Multitopic Conference, 2008
In this paper, we present a novel method to embed secret message in the cover-image so that the interceptors will not notice about the existence of the hidden data. The basic concept of the proposed method is by simple Least Significant Bit (LSB) substitution. In order to improve the quality of stego-image and to increase the secret message capacity and security level, we inspire by the work of Li and Wang [1] which splits the cover-image into n blocks of 8 X 8 pixels and the secret message into n partitions. Then we apply Particle Swarm Optimization (PSO) algorithm to search approximate optimal solutions and to find an optimal substitution matrix for transforming the secret message in each block, instead of finding only one optimal substitution matrix for the whole cover-image as in . The quality of the resulting stegoimage, the secret message capacity and the security level of the proposed method are calculated and compared to other methods. Experimental results show that the proposed method outperforms the JPEG and Quantization Table Modification (JQTM) method and the Li and Wang's work in image quality, embedding capacity and security level.
An Invisible Image Watermarking Based On Modified Particle Swarm Optimization (PSO) Algorithm
International Journal of Security and Its Applications
Tradeoff between the embedding energy of watermark and the perceptual translucence and the image fidelity following attacks represent an important issue in watermarking images. The present work introduces a brand new invisible watermarking algorithm grounded on modified Particle Swarm Optimization (PSO) algorithm with Human Visual System (HVS) model into consideration. The purpose was to improve the ownership and imperceptibility of image. In PSO invisible watermarking, the global and local characteristics of the host as well as watermark images within the Singular Value Decomposition (SVD) domain were utilized. The experimental results of Modified PSO (JPSO) algorithm demonstrate high PSNR values for the watermarked images, while the watermark remains invisible under numerous signal processing, in particular, the attack of watermark removal (also, adaptive watermarking in the different types of attacks).