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

Building a dataset for image steganography

Image steganography and steganalysis techniques discussed in the literature rely on using a dataset(s)created based on cover images obtained from the public domain, through the acquisition of images from Internet sources, or manually. This issue often leads to challenges in validating, benchmarking, and reproducing reported techniques in a consistent manner. It is our view that the steganography/steganalysis research community would benefit from the availability of common datasets, thus promoting transparency and academic integrity. In this research, we have considered four aspects: image acquisition, pre-processing, steganographic techniques, and embedding rate in building a dataset for image steganography

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

PIRANHA: an engine for a methodology of detecting covert communication via image-based steganography

In current cutting-edge steganalysis research, model-building and machine learning has been utilized to detect steganography. However, these models are computationally and cognitively cumbersome, and are specifically and exactly targeted to attack one and only one type of steganography. The model built and utilized in this thesis has shown capability in detecting a class or family of steganography, while also demonstrating that it is viable to construct a minimalist model for steganalysis. The notion of detecting steganographic primitives or families is one that has not been discussed in literature, and would serve well as a first-pass steganographic detection methodology. The model built here serves this end well, and it must be kept in mind that the model presented is posited to work as a front-end broad-pass filter for some of the more computationally advanced and directed stganalytic algorithms currently in use. This thesis attempts to convey a view of steganography and steganalysis in a manner more utilitarian and immediately useful to everyday scenarios. This is vastly different from a good many publications that treat the topic as one relegated only to cloak-and-dagger information passing. The subsequent view of steganography as primarily a communications tool useable by petty information brokers and the like directs the text and helps ensure that the notion of steganography as a digital dead-drop box is abandoned in favor of a more grounded approach. As such, the model presented underperforms specialized models that have been presented in current literature, but also makes use of a large image sample space (747 images) as well as images that are contextually diverse and representative of those seen in wide use. In future applications by either law-enforcement or corporate officials, it is hoped that the model presented in this thesis can aid in rapid and targeted responses without causing undue strain upon an eventual human operator. As such, a design constraint that was utilized for this research favored a False Negative as opposed to a False Positive - this methodology helps to ensure that, in the event of an alert, it is worthwhile to apply a more directed attack against the flagged image

Information similarity metrics in information security and forensics

We study two information similarity measures, relative entropy and the similarity metric, and methods for estimating them. Relative entropy can be readily estimated with existing algorithms based on compression. The similarity metric, based on algorithmic complexity, proves to be more difficult to estimate due to the fact that algorithmic complexity itself is not computable. We again turn to compression for estimating the similarity metric. Previous studies rely on the compression ratio as an indicator for choosing compressors to estimate the similarity metric. This assumption, however, is fundamentally flawed. We propose a new method to benchmark compressors for estimating the similarity metric. To demonstrate its use, we propose to quantify the security of a stegosystem using the similarity metric. Unlike other measures of steganographic security, the similarity metric is not only a true distance metric, but it is also universal in the sense that it is asymptotically minimal among all computable metrics between two objects. Therefore, it accounts for all similarities between two objects. In contrast, relative entropy, a widely accepted steganographic security definition, only takes into consideration the statistical similarity between two random variables. As an application, we present a general method for benchmarking stegosystems. The method is general in the sense that it is not restricted to any covertext medium and therefore, can be applied to a wide range of stegosystems. For demonstration, we analyze several image stegosystems using the newly proposed similarity metric as the security metric. The results show the true security limits of stegosystems regardless of the chosen security metric or the existence of steganalysis detectors. In other words, this makes it possible to show that a stegosystem with a large similarity metric is inherently insecure, even if it has not yet been broken

Improved steganalysis technique based on least significant bit using artificial neural network for MP3 files

MP3 files are one of the most widely used digital audio formats that provide a high compression ratio with reliable quality. Their widespread use has resulted in MP3 audio files becoming excellent covers to carry hidden information in audio steganography on the Internet. Emerging interest in uncovering such hidden information has opened up a field of research called steganalysis that looked at the detection of hidden messages in a specific media. Unfortunately, the detection accuracy in steganalysis is affected by bit rates, sampling rate of the data type, compression rates, file track size and standard, as well as benchmark dataset of the MP3 files. This thesis thus proposed an effective technique to steganalysis of MP3 audio files by deriving a combination of features from MP3 file properties. Several trials were run in selecting relevant features of MP3 files like the total harmony distortion, power spectrum density, and peak signal-to-noise ratio (PSNR) for investigating the correlation between different channels of MP3 signals. The least significant bit (LSB) technique was used in the detection of embedded secret files in stego-objects. This involved reading the stego-objects for statistical evaluation for possible points of secret messages and classifying these points into either high or low tendencies for containing secret messages. Feed Forward Neural Network with 3 layers and traingdx function with an activation function for each layer were also used. The network vector contains information about all features, and is used to create a network for the given learning process. Finally, an evaluation process involving the ANN test that compared the results with previous techniques, was performed. A 97.92% accuracy rate was recorded when detecting MP3 files under 96 kbps compression. These experimental results showed that the proposed approach was effective in detecting embedded information in MP3 files. It demonstrated significant improvement in detection accuracy at low embedding rates compared with previous work

A review and open issues of multifarious image steganography techniques in spatial domain

Nowadays, information hiding is becoming a helpful technique and fetch more attention due fast growth of using internet, it is applied for sending secret information by using different techniques. Steganography is one of major important technique in information hiding. Steganography is science of concealing the secure information within a carrier object to provide the secure communication though the internet, so that no one can recognize and detect it’s except the sender & receiver. In steganography, many various carrier formats can be used such as an image, video, protocol, audio. The digital image is most popular used as a carrier file due its frequency on internet. There are many techniques variable for image steganography, each has own strong and weak points. In this study, we conducted a review of image steganography in spatial domain to explore the term image steganography by reviewing, collecting, synthesizing and analyze the challenges of different studies which related to this area published from 2014 to 2017. The aims of this review is provides an overview of image steganography and comparison between approved studies are discussed according to the pixel selection, payload capacity and embedding algorithm to open important research issues in the future works and obtain a robust method

Computational intelligence in steganalysis environment

This paper presents gives a consolidated view of digital media steganalysis from the perspective of computational intelligence (CI). The environment of digital media steganalysis can be divided into three (3)domains which are image steganalysis, audio steganalysis, and video steganalysis. Three (3) major methods have also been identified in the computational intelligence based on these steganalysis domains which are bayesian, neural network, and genetic algorithm. Each of these methods has pros and cons. Therefore, it depends on the steganalyst to use and choose a suitable method based on their purposes and its environment

A Low Cost Image Steganalysis by Using Domain Adaptation

Information hiding and data encryption are used widely to protect data and information from anonymous access. In digital world, hiding and encrypting of the desired data into an image is a smart way to protect information with a low cost. In the digital images, steganalysis is a known method to distinguish between clean and stego images. Most of recent researches in this scope exploit feature reduction algorithms to improve the performance of correct detections. However, dimension reduction alone could not tackle the problem of steganalysis because the properties of stego images change during the steganalysis process. In this work, it is intended to propose an Image Steganalysis using visual Domain Adaptation (ISDA), which this steganalysis target images to distinguish across stego and clean images. ISDA is a dimensionality reduction approach that considers the image drifts during the steganography process in the steganalysis of target images. Moreover, ISDA employs domain invariant clustering in an embedded representation to cluster clean and stego images in the reduced subspace. The results on benchmark datasets demonstrate that ISDA thoroughly outperforms all of the state of the art methods on validation parameters, accuracy of detection and time complexit