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

An information theoretic image steganalysis for LSB steganography

Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio

Unified Description for Network Information Hiding Methods

Until now hiding methods in network steganography have been described in arbitrary ways, making them difficult to compare. For instance, some publications describe classical channel characteristics, such as robustness and bandwidth, while others describe the embedding of hidden information. We introduce the first unified description of hiding methods in network steganography. Our description method is based on a comprehensive analysis of the existing publications in the domain. When our description method is applied by the research community, future publications will be easier to categorize, compare and extend. Our method can also serve as a basis to evaluate the novelty of hiding methods proposed in the future.Comment: 24 pages, 7 figures, 1 table; currently under revie

Designing Secure and Survivable Stegosystems

Steganography, the art and science of carrying out hidden communication, is an emergingsub-discipline of information security. Unlike cryptography, steganography conceals the existenceof a secret message by embedding it in an innocuous container digital media, thereby enablingunobstrusive communication over insecure channels. Detection and extraction of steganographiccontents is another challenge for the information security professional and this activity iscommonly known as steganalysis. Recent progress in steganalysis has posed a challenge fordesign and development of stegosystems with high levels of security and survivability. In thispaper, different strategies have been presented that can be used to escape detection and foilan eavesdropper having high technical capabilities as well as adequate infrastructure. Based onthe strength and weaknesses of current steganographic schemes, ideas have been progressedto make detection and destruction of hidden information more difficult

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

Digital watermarking: a state-of-the-art review

Digital watermarking is the art of embedding data, called a watermark, into a multimedia object such that the watermark can be detected or extracted later without impairing the object. Concealment of secret messages inside a natural language, known as steganography, has been in existence as early as the 16th century. However, the increase in electronic/digital information transmission and distribution has resulted in the spread of watermarking from ordinary text to multimedia transmission. In this paper, we review various approaches and methods that have been used to conceal and preserve messages. Examples of real-world applications are also discussed.SANPAD, Telkom, Cisco, Aria Technologies, THRIPDepartment of HE and Training approved lis

Digital Image Steganography

Steganography is defined as the science of hiding or embedding data in a transmission medium. Its ultimate objectives, which are undetectability, robustness (i.e., against image processing and other attacks) and capacity of the hidden data (i.e., how much data we can hide in the carrier file), are the main factors that distinguish it from other sisters-in science. techniques, namely watermarking and Cryptography. This paper provides an overview of well known Steganography methods. It identifies current research problems in this area and discusses how our current research approach could solve some of these problems. We propose using human skin tone detection in colour images to form an adaptive context for an edge operator which will provide an excellent secure location for data hiding