A novel secure model: Image steganography with logistic map and secret key

Ulker, Mehtap; Arslan, Bilgehan

doi:10.1109/isdfs.2018.8355320

Cited by 11 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to effectively evaluate the quality of stego-images, various evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Normalized Cross Correlation (NCC), and Structural Similarity Index (SSIM) are used [97], [98]. Many image qualities of assessment metrics are used for encrypted image and any one can be utilize for measurement of the image.…”

Section: B Some Quality Assessment Metrics Analysis For Stego Imagementioning

confidence: 99%

A Comprehensive Study of Digital Image Steganographic Techniques

et al. 2023

View full text Add to dashboard Cite

Steganography surpasses other mechanisms of securing data from potential threats. The modern digital arena calls for robust information hiding techniques and, thus, it has always been a flash point for researchers and academicians. Nowadays, transmission is susceptible to numerous hacks while sharing secret information through typical correspondence channel. Accordingly, everybody needs the classification, respectability, and realness of his or her privileged information. Particularly, different techniques are used to take on these security issues like advanced declaration, computerized mark, and cryptography. Nevertheless, these strategies alone cannot be negotiated. Steganography is a revolution where current information compression, data hypothesis, spread range, and cryptography advancements are integrated to meet the requirements for protection of data over the Internet. This study investigates and critically analyses various existing cover steganography techniques and identifies the valuable region that everyone can be benefited. Moreover, we present a comprehensive overview of the fundamental concepts with in the domain of the steganographic methods and steganalysis. These systems have been depicted in numerous areas of the steganography such as spatial space, transform domain, and adaptive space. Moreover, each space has its particular traits. A few regularly involved techniques for improving the steganographic security and upgrading steganalysis capacity are elaborated, summed up; and conceivable examination patterns are talked about. We also systematically separate different methodologies in our review and show their pros and cons, qualities, challenges and significance.INDEX TERMS Steganography, data concealing, cover objects, image quality assessment metrics.

show abstract

Section: B Some Quality Assessment Metrics Analysis For Stego Imagementioning

confidence: 99%

A Comprehensive Study of Digital Image Steganographic Techniques

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The steganography consists of several basic components, which are considered essential components, starting with the original image before embedding it, which is called the cover image, and the text to be included within the image is called the secret message, which is supposed to be transferred to the binary system [12]. The embedding process, which reflects the proposed method, is stored in the key called the stego key.…”

Section: Steganographymentioning

confidence: 99%

Improve steganography system using agents software based on statistical and classification technique

Halboos¹,

Al-Bakry²

2023

Bulletin EEI

View full text Add to dashboard Cite

In digital communications, information security is a paramount necessity. In the hiding algorithm, there are three basic parameters: security, capacity, and imperceptibility. Therefore, there are many ways to design the steganography algorithm, such as least significant bit (LSB), discrete wave transformation (DWT), and discrete cosine transform (DCT). The aim of this paper is to improve agent software design based on a steganography system. It proposed an agent system based on a support vector machine (SVM) classifier to hide a secret message in a certain cover image. The common dataset for steganography uses 80% training and 20% testing to get accurate results. Developing an agent system depends on six statistical parameters such as energy, standard deviation, histogram, variance, mean, and entropy. This resulted in features classified by the SVM classifier to predict the best cover image to be nominated for embedding. Worthy results were obtained in terms of imperceptibility, attack, and cover image prediction by statistical issues.

show abstract

“…Karakteristik chaos adalah sensitivitas terhadap kondisi awal, berkelakuan acak, dan tidak memiliki periode berulang.Logistic map merupakan salah satu fungsi chaos yang banyak digunakan dalam teknik penyisipan data karena sederhana dan sangat cepat tetapi fungsi ini sendiri memiliki ruang kunci yang kecil [5]. Permasalahan ruang kunci merupakan faktor penting dalam metode keamanan [6]. Untuk meningkatkan keamanan, dilakukan sebuah modifikasi pada logistic map untuk meningkatkan ruang kunci dengan menambahkan tiga variabel baru berupa , ,dan sebagai parameter baru.…”

Section: Pendahuluanunclassified

Pengamanan Citra Dengan Kombinasi Modified Serpent IWT Dengan Modified Logistic Chaotic Map

Lysander¹,

Simarmata²,

Lusandy³

et al. 2021

JATISI

View full text Add to dashboard Cite

Abstrak Algoritma kriptografi dapat digunakan untuk mengamankan citra. Salah satu algoritma kriptografi yang dapat digunakan untuk mengenkripsi citra adalah algoritma Serpent. Namun, algoritma Serpent memerlukan proses enkripsi dan dekripsi yang lama. Selain itu, pengenkripsian saja tidaklah cukup karena akan menimbulkan kecurigaan sehingga informasi akan rentan dicuri. Maka diperlukan teknik steganografi seperti metode Integer Wavelet Transform (IWT). Citra rahasia akan dienkripsi terlebih dahulu dengan menggunakan metode Modified Serpent, dimana modifikasi Serpernt mengubah proses transformasi substitusi byte (DES S-box) menjadi list bilangan prima 257 yang memiliki 128 generator untuk digunakan sebagai kunci di setiap putarannya. Pada proses penyisipan dengan metode IWT, perlu ditentukan posisi bit dan piksel pada cover image yang akan disisipkan sebuah pesan. Untuk menentukan posisi tersebut, maka digunakanlah fungsi chaos. Setelah itu, dapat dilakukan proses ekstraksi terhadap citra stego yang dihasilkan pada proses penyisipan untuk memperoleh kembali bit yang tersimpan didalamnya. Kumpulan bit tersebut dapat didekripsi sehingga akan diperoleh kembali citra rahasia semula.

show abstract

A novel secure model: Image steganography with logistic map and secret key

Cited by 11 publications

References 17 publications

A Comprehensive Study of Digital Image Steganographic Techniques

A Comprehensive Study of Digital Image Steganographic Techniques

Improve steganography system using agents software based on statistical and classification technique

Pengamanan Citra Dengan Kombinasi Modified Serpent IWT Dengan Modified Logistic Chaotic Map

Contact Info

Product

Resources

About