scholarly journals A Linguistic Steganalysis Approach Base on Source Features of Text and Immune Mechanism

2017 ◽  
Vol 10 (4) ◽  
pp. 60
Author(s):  
Licai Zhu
Keyword(s):  
Statistical Features ◽  
Immune Mechanism ◽  
Hidden Information ◽  
Detection Mechanism ◽  
Is Success ◽  
Plain Text ◽  
Segment Size ◽  
Challenge Area ◽  
Blind Steganalysis ◽  
Steganalysis Method

Linguistic steganalysis is a technique that discovering potentially hidden information embedded through using linguistically in plain text using. Varieties of syntax and multi-meanings of semantics for linguistics augment the difficulty of linguistic steganalysis intensely, thereby it is a challenge area. In this paper, we propose a novel steganalysis method for linguistics based on immune. This method has two attributions: i). basis statistical features of text are employed for blind steganalysis ii). immune technique is chosen to build a two-level detection mechanism to detect two categories of stego text respectively, one of which is Success-Stego-text and another is False-Stego-text. Appropriate detections are generated and preferable features are signed. Experiments prove the approach has higher accuracy than current steganalysis algorithms. Especially when the segment size of text is greater than 3kB, the accuracies of detecting for natural text and stego text are both more than 95%. 

Statistical Analysis for Radiologists’ Interpretations Variability in Mammograms

2012 ◽  
Vol 1 (4) ◽  
pp. 28-46
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Breast Cancer ◽  
Benign Tumors ◽  
Statistical Features ◽  
Image Processing Algorithm ◽  
Statistical Tools ◽  
Detection Mechanism ◽  
Mammographic Images ◽  
Female Breast ◽  
Conventional Mammography ◽  
The Mean

Conventional mammography is considered the modality of choice for the detection of breast cancer. The process involves a human radiologist visually diagnosing the mammogram, which causes limitations such as missing a cancer and/or diagnosing a false cancer. Another disadvantage of conventional mammography is the variability among screening radiologists in interpreting mammographic images. The objectives of this study are to verify this variability and to develop an image processing algorithm that can automatically detect benign tumors of the female breast. A sample of ten digital mammograms obtained from the MiniMIAS database was distributed to four different radiologists in order to verify the variability among them. Furthermore, three algorithms were developed in order to automatically detect benign tumors of the female breast. The proposed algorithms were based on combinations of certain statistical features and were tested on the same sample of images. Results showed that the detection mechanism using the proposed algorithms was acceptable despite the fact that they exhibited a few errors. It was concluded that the use of a combination of the mean and median statistical tools is effective in assisting radiologists in interpreting mammographic images containing benign tumors.

On Steganalysis and Clean Image Estimation

2011 ◽  
pp. 212-244
Author(s):  
Christopher B. Smith ◽  
Sos S. Agaian
Keyword(s):  
Digital Media ◽  
Image Model ◽  
Hidden Information ◽  
Art And Science ◽  
Image Estimation ◽  
The Future ◽  
Blind Steganalysis ◽  
Digital Steganography

Steganalysis is the art and science of detecting hidden information. Modern digital steganography has created techniques to embed information near invisibly into digital media. This chapter explores the idea of exploiting the noise-like qualities of steganography. In particular, the art of steganalysis can be defined as detecting and/or removing a very particular type of noise. This chapter first reviews a series of steganalysis techniques including blind steganalysis and targeted steganalysis methods, and highlights how clean image estimation is vital to these techniques. Each technique either implicitly or explicitly uses a clean image model to begin the process of detection. This chapter includes a review of advanced methods of clean image estimation for use in steganalysis. From these ideas of clean image estimation, the problems faced by the passive warden can be posed in a systematic way. This chapter is concluded with a discussion of the future of passive warden steganalysis.

scholarly journals Steganalysis of Inactive Voice-Over-IP Frames Based on Poker Test

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 336
Author(s):  
Jie Liu ◽  
Hui Tian ◽  
Chin-Chen Chang ◽  
Tian Wang ◽  
Yonghong Chen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Voice Over Ip ◽  
Experimental Results ◽  
Support Vector ◽  
Voice Over Internet Protocol ◽  
Statistical Features ◽  
Test Statistics ◽  
Low Bit Rate ◽  
Audio Streams ◽  
Steganalysis Method

This paper concentrates on the detection of steganography in inactive frames of low bit rate audio streams in Voice over Internet Protocol (VoIP) scenarios. Both theoretical and experimental analyses demonstrate that the distribution of 0 and 1 in encoding parameter bits becomes symmetric after a steganographic process. Moreover, this symmetry affects the frequency of each subsequence of parameter bits, and accordingly changes the poker test statistical features of encoding parameter bits. Employing the poker test statistics of each type of encoding parameter bits as detection features, we present a steganalysis method based on a support vector machine. We evaluate the proposed method with a large quantity of speech samples encoded by G.723.1 and compare it with the entropy test. The experimental results show that the proposed method is effective, and largely outperforms the entropy test in any cases.

A Steganalysis Method for 2D Engineering Graphics Based on the Statistic of Geometric Features

2011 ◽  
Vol 3 (2) ◽  
pp. 35-40
Author(s):  
Fei Peng ◽  
Honglin Li
Keyword(s):  
Experimental Results ◽  
Statistical Characteristics ◽  
Geometric Features ◽  
Hidden Information ◽  
Engineering Graphics ◽  
Detection Model ◽  
Straight Line ◽  
Steganalysis Method

Aiming at hiding information in 2D engineering graphics based on geometric features, a steganalysis method is proposed in this paper. First, the authors obtained the number of 2D engineering graphics’ strictly vertical and horizontal lines and identify the number of horizontal and vertical lines which are deviated from the straight line within a certain range. Subsequently, the authors selected the ratio between the deviated lines and the normal lines as the statistical characteristics. Finally, a detection model was constructed based on the hypothesis. Experimental results show that the algorithm can detect hidden information in the 2D engineering graphics effectively.

Statistical Analysis for Radiologists’ Interpretations Variability in Mammograms

2013 ◽  
pp. 753-768
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Breast Cancer ◽  
Benign Tumors ◽  
Statistical Features ◽  
Image Processing Algorithm ◽  
Statistical Tools ◽  
Detection Mechanism ◽  
Mammographic Images ◽  
Female Breast ◽  
Conventional Mammography ◽  
The Mean

Conventional mammography is considered the modality of choice for the detection of breast cancer. The process involves a human radiologist visually diagnosing the mammogram, which causes limitations such as missing a cancer and/or diagnosing a false cancer. Another disadvantage of conventional mammography is the variability among screening radiologists in interpreting mammographic images. The objectives of this study are to verify this variability and to develop an image processing algorithm that can automatically detect benign tumors of the female breast. A sample of ten digital mammograms obtained from the MiniMIAS database was distributed to four different radiologists in order to verify the variability among them. Furthermore, three algorithms were developed in order to automatically detect benign tumors of the female breast. The proposed algorithms were based on combinations of certain statistical features and were tested on the same sample of images. Results showed that the detection mechanism using the proposed algorithms was acceptable despite the fact that they exhibited a few errors. It was concluded that the use of a combination of the mean and median statistical tools is effective in assisting radiologists in interpreting mammographic images containing benign tumors.

scholarly journals Analysis review on linguistic steganalysis

2020 ◽  
Vol 17 (2) ◽  
pp. 950
Author(s):  
Syiham Mohd Lokman ◽  
Aida Mustapha ◽  
Azizan Ismail ◽  
Roshidi Din
Keyword(s):  
Digital Media ◽  
Recent Work ◽  
Future Research ◽  
Video And Audio ◽  
Real Practice ◽  
Blind Steganalysis ◽  
Steganalysis Method

<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>

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

2020 ◽  
Vol 7 (4) ◽  
pp. 787
Author(s):  
Nurmi Hidayasari ◽  
Imam Riadi ◽  
Yudi Prayudi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Error Probability ◽  
Learning Performance ◽  
Net Method ◽  
Blind Steganalysis ◽  
Steganalysis Method

<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>

A hybrid ontology-based information extraction system

2016 ◽  
Vol 42 (6) ◽  
pp. 798-820 ◽  
Author(s):  
Fernando Gutierrez ◽  
Dejing Dou ◽  
Stephen Fickas ◽  
Daya Wimalasuriya ◽  
Hui Zong
Keyword(s):  
Information Extraction ◽  
Error Detection ◽  
Contextual Information ◽  
Implementation Strategies ◽  
Extraction Process ◽  
Domain Ontology ◽  
Extraction System ◽  
Detection Mechanism ◽  
Plain Text ◽  
Task Ontology

Information Extraction is the process of automatically obtaining knowledge from plain text. Because of the ambiguity of written natural language, Information Extraction is a difficult task. Ontology-based Information Extraction (OBIE) reduces this complexity by including contextual information in the form of a domain ontology. The ontology provides guidance to the extraction process by providing concepts and relationships about the domain. However, OBIE systems have not been widely adopted because of the difficulties in deployment and maintenance. The Ontology-based Components for Information Extraction (OBCIE) architecture has been proposed as a form to encourage the adoption of OBIE by promoting reusability through modularity. In this paper, we propose two orthogonal extensions to OBCIE that allow the construction of hybrid OBIE systems with higher extraction accuracy and a new functionality. The first extension utilizes OBCIE modularity to integrate different types of implementation into one extraction system, producing a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. The second extension is a novel ontology-based error detection mechanism. Following a heuristic approach, we can identify sentences that are logically inconsistent with the domain ontology. Because the implementation strategy for the extraction of a concept is independent of the functionality of the extraction, we can design a hybrid OBIE system with concepts utilizing different implementation strategies for extracting correct or incorrect sentences. Our evaluation shows that, in the implementation extension, our proposed method is more accurate in terms of correctness and completeness of the extraction. Moreover, our error detection method can identify incorrect statements with a high accuracy.

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