scholarly journals Least Significant Bit Image Steganography using Particle Swarm Optimization and Optical Pixel Adjustment

2012 ◽  
Vol 55 (2) ◽  
pp. 20-25 ◽  
Author(s):  
Parisa Gerami ◽  
Subariah Ebrahim ◽  
Morteza Bashardoost
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Image Steganography ◽  
Least Significant Bit ◽  
Swarm Optimization

scholarly journals An Image Steganography using Particle Swarm Optimization and Transform Domain

2018 ◽  
Vol 7 (2.24) ◽  
pp. 474 ◽  
Author(s):  
Sanjutha MK
Keyword(s):  
Particle Swarm Optimization ◽  
Cost Function ◽  
Fitness Function ◽  
Particle Swarm ◽  
Image Steganography ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Secret Image ◽  
The Cost

As information technology is growing tremendously, one of the major concern is information security. A technique called image steganography is used to provide better security and for safeguarding the information. In image steganography, a secret image is put into recipient image so that only the receiver and sender will be aware of the secret message. Here in this paper, a secure, optimized scheme called particle swarm optimization is used to select the pixel efficiently for embedding the secret image in to cover image. PSO(Particle Swarm Optimization) decides pixel using fitness function which is based on the cost function. Cost function calculates entropy, edge and pixels intensity to evaluate fitness. Also, a technique called discrete wavelet transform has been employed to achieve robustness and statistical undetectability. The main aim of the proposed paper is to make better security and to obtain efficient PSNR and MSE values  

scholarly journals Steganografi Menggunakan Blok Permutasi dan Algoritma Particle Swarm Optimization (PSO)

2016 ◽  
Vol 1 (2) ◽  
pp. 57
Author(s):  
Husna Aydadenta ◽  
Danang Triantoro M ◽  
Jondri Nasri
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Least Significant Bit ◽  
Swarm Optimization

<span>Pada saat ini perkembangan teknologi sudah semakin maju, banyak hal yang dipermudahkan <span>oleh teknologi, salah satu nya dalam komunikasi. Komunikasi melalui jaringan internet sudah menjadi pilihan utama karena kemudahan dan kecepatannya. Akan tetapi keamanan <span>datanya jarang diperhatikan oleh para <span><em>users</em><span>, sehingga dibutuhkan teknik keamanan data <span>yang aman untuk menjaga data ketika melakukan komunikasi. Salah satu tekniknya adalah<span> steganografi. Steganografi merupakan teknik <span>untuk menyembunyikan informasi rahasia ke dalam beberapa media file. Pada penelitian <span>ini dilakukan proses steganografi menggunakan metode blok permutasi dan algoritma optimasi PSO. <span>Pesan yang akan disisipkan didalam citra digital akan <span>dikonversi kedalam biner, selanjutnya akan dilakukan proses pra-steganografi dengan metode <span>blok permutasi, sehingga pesan informasi akan teracak oleh metode tersebut. Selanjutnya <span>algoritma PSO akan melakukan proses optimasi atau pencarian solusi terbaik untuk penyisipan pesan tiap piksel, tujuannya agar performansi citra tetap baik. Penyisipan pesan atau <span>informasi dilakukan dengan pendekatan <span><em>Least Significant Bit </em><span>(LSB). Hasil akhir yang didapat <span>menunjukan nilai performansi kualitas citra terbaik 60.4507 dB yaitu pada saat jumlah partikel <span>80 dan maksimum iterasi 50 dengan persentase error 5.86%. Sedangkan hasil performansi <span>kualitas citra dengan menggunakan teknik LSB biasa yaitu 54.9364 dB dengan persentase <span>error 21.03%. Hal ini menunjukan performansi kualitas citra dengan menggunakan metode <span>blok permutasi dan algoritma PSO lebih baik dari pada menggunakan teknik LSB biasa.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /></span></span></span></span></span>

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.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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