Cover-aware Steganalysis

The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in cover-aware scenarios, we will call this non-blind steganalysis. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis. Therefore we evaluated common stego-tools and their embedding algorithms and established a dataset of 353110 images. The images have been applied to test the potency of the improved methods of the non-blind steganalysis. The results show that the accuray can be significantly improved by using cover-images to produce reference images. Also the aggregation of the outcomes has shown to have a positive impact on the accuracy. Particularly noteworthy is the correlation between the qualities of the stego- and coverimages. Only by consindering both, the accuracy could strongly be improved. Interestingly the difference between both qualities also has a deep impact on the results.

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>

Steganalysis Using Yedrodj-net net's Convolutional Neural Networks (CNN) Method on Steganography Tools

Steganalysis method is used to detect the presence or absence of steganography files or can be referred to anti-steganography. Steganalysis can be used for positive purposes, which is to know the weaknesses of a steganography method, so that improvements can be made. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. Blind steganalysis is difficult to implement, but then machine learning techniques emerged that could be used to create a detection model using experimental data, one of which is Convolutional Neural Networks (CNN). A study proposes that the CNN method can detect steganography files using the latest method with a low error probability value compared to other methods, CNN Yedroudj-net. As one of the steganalysis methods with the latest machine learning steganalysis techniques, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of many tools commonly used for steganography applications. Knowing the performance of CNN Yedroudj-net on several steganography tools is very important, to measure the level of ability in terms of steganalysis of some of these 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 study will use five tools that are Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which are not known exactly what steganography methods are used on the tools. Yedroudj-net method will be implemented in the steganography file from the output of the five tools. Then a comparison with the popular steganalysis tool is used, StegSpy. The results show that Yedroudj-net is quite capable of detecting the presence of steganography files, slightly better than StegSpy.

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>

Blind Steganalysis for JPEG Images using SVM and SVM-PSO Classifiers

Blind steganalysis or the universal steganalysis helps to identify hidden information without previous knowledge of the content or the embedding technique. The Support Vector Machine (SVM) and SVM- Particle Swarm Optimization (SVM-PSO) classifiers are adopted for the proposed blind steganalysis. The important features of the JPEG images are extracted using Discrete Cosine Transform (DCT). The kernel functions used for the classifiers in the proposed work are the linear, epanechnikov, multi-quadratic, radial, ANOVA and polynomial. The proposed work uses linear, shuffle, stratified and automatic sampling techniques. The proposed work employs four techniques for image embedding namely, Least Significant Bit (LSB) Matching, LSB replacement, Pixel Value Differencing (PVD) and F5 and applies 25% embedding. The data to the classifier is split as 80:20 for training and testing and 10-fold cross validation is carried out.