Nested Two-Layer RGB Based Reversible Image Steganography Method

In this study, a new reversible image steganography method based on Red-Green-Blue (RGB) which hides thecolored image into the colored images in two layers nested is proposed. The proposed method hides the 24-bitimage to be hidden by hiding two layers of data firstly in the resized version of the cover image with the LSBmethod, and then hides the resized cover image to the original cover image with the 4-bit method. The proposedmethod offers a secure communication environment as it hides the hidden image in two layers. When thirdparties extract data by using the LSB method, they only access the resized version of the cover image. The 4-bitmethod divides the image to be hidden into 8-bit segments. While the first 4 bits, which are the most importantbits of 8-bit data, are hidden directly, 4 bits that can be neglected with less significance are completed by roundingat approximate value through the method function. In this way, since the 8-bit data is reduced to 4-bits, themethod performs lossy hiding, but doubles the hiding capacity. Peak signal to noise ratio (PSNR), structuralsimilarity quality criterion (SSIM) and chi-square steganalysis method, which are frequently used in the literature,are used to measure the immunity level of the proposed method. When it is concealed at the same ratewith the LSB method and the proposed method, a higher measurement value is obtained in the PSNR imagecriterion, which is 1.2 dB, SSIM 0.0025, BER 0.0129 and NCC image criterion 0.00027. In additional, it wasshown that the proposed method achieved more successful results in chi-square steganalysis and histogramtests compared to the traditional LSB method.

Steganalisis Blind dengan Metode Convolutional Neural Network (CNN) Yedroudj- Net terhadap Tools Steganografi

<p>Steganalisis digunakan untuk mendeteksi ada atau tidaknya file steganografi. Salah satu kategori steganalisis adalah blind steganalisis, yaitu cara untuk mendeteksi file rahasia tanpa mengetahui metode steganografi apa yang digunakan. Sebuah penelitian mengusulkan bahwa metode Convolutional Neural Networks (CNN) dapat mendeteksi file steganografi menggunakan metode terbaru dengan nilai probabilitas kesalahan rendah dibandingkan metode lain, yaitu CNN Yedroudj-net. Sebagai metode steganalisis Machine Learning terbaru, diperlukan eksperimen untuk mengetahui apakah Yedroudj-net dapat menjadi steganalisis untuk keluaran dari tools steganografi yang biasa digunakan. Mengetahui kinerja CNN Yedroudj-net sangat penting, untuk mengukur tingkat kemampuannya dalam hal steganalisis dari beberapa tools. Apalagi sejauh ini, kinerja Machine Learning masih diragukan dalam blind steganalisis. Ditambah beberapa penelitian sebelumnya hanya berfokus pada metode tertentu untuk membuktikan kinerja teknik yang diusulkan, termasuk Yedroudj-net. Penelitian ini akan menggunakan lima alat yang cukup baik dalam hal steganografi, yaitu Hide In Picture (HIP), OpenStego, SilentEye, Steg dan S-Tools, yang tidak diketahui secara pasti metode steganografi apa yang digunakan pada alat tersebut. Metode Yedroudj-net akan diimplementasikan dalam file steganografi dari output lima alat. Kemudian perbandingan dengan tools steganalisis lain, yaitu StegSpy. Hasil penelitian menunjukkan bahwa Yedroudj-net bisa mendeteksi keberadaan file steganografi. Namun, jika dibandingkan dengan StegSpy hasil gambar yang tidak terdeteksi lebih tinggi.</p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p><em>Steganalysis is used to detect the presence or absence of steganograpy files. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. A study proposes that the Convolutional Neural Networks (CNN) method can detect steganographic files using the latest method with a low error probability value compared to other methods, namely CNN Yedroudj-net. As the latest Machine Learning steganalysis method, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of commonly used steganography tools. Knowing the performance of CNN Yedroudj-net is very important, to measure the level of ability in terms of steganalysis from several tools. Especially so far, Machine Learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This research will use five tools that are good enough in terms of steganography, namely Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which is not known exactly what steganography methods are used on the tool. The Yedroudj-net method will be implemented in a steganographic file from the output of five tools. Then compare with other steganalysis tools, namely StegSpy. The results showed that Yedroudj-net could detect the presence of steganographic files. However, when compared with StegSpy the results of undetected images are higher.</em></p>

Analysis review on linguistic steganalysis

<span>Steganography and steganalysis are essential topics for hiding information. Steganography is a technique of conceal secret messages by transmitting data through different domains. Its objective is to avoid discovery of secret messages. Steganalysis, meanwhile, is a method for locating the secret messages contained in the stego text. The objective of steganalysis is to find concealed data and to break the security of its domains. Steganalysis can be categorized into two types: targeted steganalysis and blind steganalysis. Steganography and steganalysis both have domains that are split into natural, also known as linguistic and digital media. There are three kinds of digital media which are picture, video and audio. The aim of this paper is to provide a survey on different linguistic steganalysis techniques used to find secret messages. This paper also highlighted two type of steganalysis method that are used in research and real practice. The discussion include findings on the most recent work on linguistic steganalysis techniques. This review hoped to help future research for improving and enhancing steganalytic capabilities.</span>