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Image Steganography And Steganalysis.pdf

In the last few years, we have seen many new and powerful steganography and steganalysis techniques reported in the literature. In the following paper we go over some general concepts and ideas that apply to steganography and steganalysis. Specifically we establish a framework and define notion of security for a steganographic system. We show how conventional definitions do not really adequately cover image steganography and an provide alternate definition. We also review some of the more recent image steganography and steganalysis techniques.

Image Steganography and Steganalysis.pdf

Distributed steganography is an approach to concealing the secret in several files, leaving fewer traces than the classical approach. Recent work proposed by Moyou and Ndoumdan have improved this approach by preserving the integrity of these files in a multi-cloud storage environment. However, the approach requires a large size of the stego-key and the management of several cloud storage environments. Our contribution consists is to improve this approach by using a single cloud storage environment and reducing the size of the stego-key. In this work, a single cloud storage environment is used to solve the problems of managing several credentials, monetary costs and data controls associated with multi-cloud storage environments. The comparisons showed interesting results with simpler operations to be performed by the participants during the process.

When the scheme uses several cover media for the embedding of the secret, the approach is called distributed steganography[10]. In this approach, the secret is split into several shares that are then embedded into multiple carrier files. The main interest being to make the detection of the entire secret message extremely difficult. The embedding strategies used are based on the modification of each carrier file. However, these modifications can reveal the presence of a secret, through methods of steganalysis[11]. The carrier files of the secret are generally stored in a cloud storage environment for integrity and confidentiality requirements after embedding of the secret[12].

Moyou and Ndoundam[13] have proposed a new paradigm of steganography transparent to any attacker and resistant to the detection and extraction of the secret. The secret was distributed in a multi-cloud storage environment through several file extensions, and the use of a multi-cloud storage environment allowed to mask the presence of a communication channel between the communicating parties. The different files used were considered as a pointer to the secret data and constituted elements of the stego-key. Thus the proposed approach considered: the management of several cloud storage environments, a large size of the stego-key due to different lists of files and different credentials in the cloud accounts.

Distributed steganography refers to the distribution of the secret into several parts which are then embedded into several cover media. In this approach, the secret is shared between several independent senders and a single receiver which receives the union of secret inputs in the communication[14]. The most commonly used cover media are images for their large data redundancy. The process requires meticulous modifications of these images, in order to go unnoticed to an unauthorized user in the communication[15]. The success of this approach lies in a good visual imperceptibility and a sufficient amount of payload[16].

Visual imperceptibility lies in the undetectability of a communication, while the payload guarantees a great capacity of secret that can be concealed[17]. Several approaches transit with visual imperceptibility as an indicator of images distortion to avoid detection of a secret message concealed[18, 19]. Others approaches use the distribution of the payload in the images[20, 21]. While hybrid approaches merge the features of several images[22] or combine the texture and payload associated with several images [23]. The interest of these approaches lies in a better resistance of the blind universal pooled steganalysis compared to other existing approaches.

Distributed steganography presents an improvement on classical steganography by concealing the secret in multiple cover media, making detection of the secret extremely difficult. However, modifications made to the cover media present limitations when setting up a process of steganalysis of these media. Indeed, several works in steganalysis on images are able to detect the presence of a secret and extract it. In general, the process is categorized into two types. One is targeted while the other is blind. Targeted steganalysis refers to an attack on a specific secret embedding algorithm[37, 38]. Blind steganalysis refers to an attack on several types of secret embedding algorithms, in which the goal is to classify the original files and stego files[39, 40].

The distributed data hiding model in a multi-cloud storage environment proposed by Moyou and Ndoundam [13], presents a new paradigm of distributed steganography that preserves the integrity of the files carrying the secret. In this model, the secret is distributed in different multimedia files that carry information of the secret message without being modified. The different multimedia files are stored in different cloud environments that mask the presence of a communication channel. The sender conceals the secret in different cloud storage environments, while the receiver retrieves the secret based on the stego-key elements. The integrity of the files being preserved, the model is more robust against steganalysis processes.

This work is part of the research of a distributed steganography paradigm using the concept of indirection on different multimedia files. Future improvements of the scheme will be to take no element in the key and to propose other more robust schemes in case of access of a spy in the cloud environment.

1. Steganography:Steganography is a method in which secret message is hidden in a cover media. Steganography means covered writing. Steganography is the idea to prevent secret information by creating the suspicion. Steganography is less popular than Cryptography. In steganography, structure of data is not usually altered.The forms of steganography are:

This book describes image steganography algorithms and one steganalysis algorithm. The steganography algorithms are primarily based on restoring the statistics of a image after embedding data in them which results in low detection rate as compared to other existing algorithms.

The steganalysis algorithm uses second order statistical features along with machine learning to train and predict if an image contains embedded message. Steganography can be used in many applications. In commercial sector, data hiding techniques can be used to enforce copyright on a digital medium. The access control information can be embedded inside the digital medium which can eventually be verified by the media player before playing the file.

This book provides the first general framework, based on universal statistical properties of natural images, of detecting tampering and authenticating digital images that has been successfully applied to three problems in digital image forensics.

Pixel-value differencing (PVD) steganography is a popular spatial domain technology. Several PVD-based studies have proposed extended PVD steganography methods. The majority of these studies have verified their security against the regular-singular (RS) analysis. However, RS analysis is aimed at the feature of the least significant bit substitution method, which is relatively less significant for PVD steganography. The pixel difference histogram (PDH) is generally utilized to attack PVD steganography. If the embedding capacity is high, then the features on the PDH are evident; otherwise, the features are less obvious. In this paper, we propose a statistical feature-based steganalysis technique for the original PVD steganography. Experimental results demonstrate that, compared with existing steganalysis technique with weighted stego-image (WS) method, the proposed method effectively detects PVD steganography at low embedding ratios, such that there is no need of using the original embedding parameters. Furthermore, the accuracy and precision of the method are better than those of existing PVD steganalysis techniques. Therefore, the proposed method contributes to the security analysis of the original PVD steganography as an alternative to the commonly used RS, PDH and WS attack techniques.

Cryptography and steganography are used to protect and secure information. Encryption algorithms protect secret data by converting it into unreadable data; however, such unreadable and incomprehensible data are likely to draw the attention of criminals. Steganography can conceal data in carriers without changing the original data. Common carriers include videos, audios, texts, and images. Compared with other carriers, digital images have considerable amount of redundant space. Therefore, digital images are commonly used for steganography and are called cover images (before embedding secret messages); similarly, images with embedded messages are called stego images. Steganography techniques can be categorized into spatial and frequency domains. In terms of image quality, the embedding capacity in the spatial domain is higher than that in the frequency domain. Therefore, the spatial domain is suitable for a large number of embedded messages. In the spatial domain, the least significant bit (LSB) substitution steganography [1], proposed in 1996, is the most popular steganography technique. Here, the LSB bit of a pixel value is directly substituted by secret bitstreams. However, LSB substitution steganography can be detected using regular-singular (RS) analysis [2].

Pixel-value differencing (PVD) steganography [3] was developed based on the edge areas in an image; it can conceal more secret messages compared to those in smooth areas. Based on this principle, PVD steganography determines the amount of messages to be embedded depending on the value of the difference between adjacent pixels. Although this method provides a high embedding capacity and invisibility, it creates a step-like shape in the pixel difference histogram (PDH), and embedded messages may be detected. A modified PVD (MPVD) steganography was proposed by Zhang et al. [4], which dynamically generates the PVD interval range to improve security.


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