Universal Image Steganalytic Method

In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods

Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

Steganography Images Detection using Different Steganalysis Techniques with Markov Chain Features

Steganography is the art of covered or hidden writing It is used for criminal activities applications environment In this paper we focus on implementation of effective detection technique is an essential task in digital images Previously many number of detection techniques are available for steganography images After implementation of all approaches also again some challenges are available This paper presents comparative study in between different steganalysis techniques Different techniques are providing different results Analyze of all techniques detection and embedding performance results Finally we can decide one best steganalysis technique It saves time and increases accuracy compare to all previous method

Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files

Steganalysis is the art of detecting the presence of hidden data in files. In the last few years, there have been a lot of methods provided for steganalysis. Each method gives a good result depending on the hiding method. This paper aims at the evaluation of five universal steganalysis techniques which are “Wavelet based steganalysis”, “Feature Based Steganalysis”, “Moments of characteristic function using wavelet decomposition based steganalysis”, “Empirical Transition Matrix in DCT Domain based steganalysis”, and “Statistical Moment using jpeg2D array and 2D characteristic function”. A large Dataset of Images -1000 images- are subjected to three types of steganographic techniques which are “Outguess”, “F5” and “Model Based” with the embedding rate of 0.05, 0.1, and 0.2. It was followed by extracting the steganalysis feature used by each steganalysis technique for the stego images as well as the cover image. Then half of the images are devoted to train the classifier. The Support vector machine with a linear kernel is used in this study. The trained classifier is then used to test the other half of images, and the reading is reported The “Empirical Transition Matrix in DCT Domain based steganalysis” achieves the highest values among all the properties measured and it becomes the first choice for the universal steganalysis technique

Universal Steganalysis of Digital Image Based on Frequency Domain Partition

互联网技术在不断发展的同时，信息安全问题也显得愈为重要。信息隐藏技术作为信息安全领域的重要研究课题一直为人们所关注。隐写技术是信息隐藏的重要研究方向，它是将需要传输的秘密信息隐藏于看似无关的数字载体中，且不被第三者发现或截取，以达到秘密通信的目的。而隐写分析技术是对嵌入的秘密信息进行检测和提取，防止有害信息的传输给国家和人民带来损失，具有现实意义。本文主要研究的是以JPEG图像为载体的隐写分析技术。 目前，隐写分析技术可以分为两大类：一是特定隐写检测技术，二是盲检测技术。其中，数字图像盲检测技术具有更强的通用性。本文研究的是针对DCT域隐写算法的盲检测技术。文章首先对隐写系统与隐写分析系统做...The Internet technology continues to develop,at the same time, the problem of information security becomes more important. Encryption technology is getting more and more attention which is used to ensure that an important means of information security.The steganography technology because of its particularity is important. The steganography technology is fairly important owing to its particularity....学位：工学硕士院系专业：信息科学与技术学院_通信与信息系统学号：2332012115296

A Learning-Based Steganalytic Method against LSB Matching Steganography

This paper considers the detection of spatial domain least significant bit (LSB) matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature