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Showing papers on "Steganography published in 2011"


Journal ArticleDOI
TL;DR: This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (nonbinary) embedding operation and reports extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.
Abstract: This paper proposes a complete practical methodology for minimizing additive distortion in steganography with general (nonbinary) embedding operation. Let every possible value of every stego element be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender (maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of performance, the nonbinary case is decomposed into several binary cases by replacing individual bits in cover elements. The binary case is approached using a novel syndrome-coding scheme based on dual convolutional codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results in steganographic applications while having linear time and space complexity w.r.t. the number of cover elements. We report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel. Practical merit of this approach is validated by constructing and testing adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes used in steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using this framework.

726 citations


Proceedings Article
01 Apr 2011
TL;DR: A survey on steganography and steganalysis for digital images, mainly covering the fundamental concepts, the progress of steganographic methods for images in spatial representation and in JPEG format, and the development of the corresponding steganalytic schemes.
Abstract: Steganography and steganalysis are important topics in information hiding. Steganography refers to the technology of hiding data into digital media without drawing any suspicion, while steganalysis is the art of detecting the presence of steganography. This paper provides a survey on steganography and steganalysis for digital images, mainly covering the fundamental concepts, the progress of steganographic methods for images in spatial representation and in JPEG format, and the development of the corresponding steganalytic schemes. Some commonly used strategies for improving steganographic se- curity and enhancing steganalytic capability are summarized and possible research trends are discussed.

417 citations


Proceedings ArticleDOI
01 Dec 2011
TL;DR: A best approach for Least Significant Bit (LSB) based on image steganography that enhances the existing LSB substitution techniques to improve the security level of hidden information and the Peak Signal-to-Noise Ratio (PSNR) to measure the quality of the stego images.
Abstract: This paper introduces a best approach for Least Significant Bit (LSB) based on image steganography that enhances the existing LSB substitution techniques to improve the security level of hidden information. It is a new approach to substitute LSB of RGB true color image. The new security conception hides secret information within the LSB of image where a secret key encrypts the hidden information to protect it from unauthorized users. In general, in LSB methods, hidden information is stored into a specific position of LSB of image. For this reason, knowing the retrieval methods, anyone can extract the hidden information. In our paper, hidden information is stored into different position of LSB of image depending on the secret key. As a result, it is difficult to extract the hidden information knowing the retrieval methods. We have used the Peak Signal-to-Noise Ratio (PSNR) to measure the quality of the stego images. The value of PSNR gives better result because our proposed method changes very small number of bits of the image. The obtained results show that the proposed method results in LSB based image steganography using secret key which provides good security issue and PSNR value than general LSB based image steganography methods.

207 citations


Journal ArticleDOI
TL;DR: It is revealed that, contrary to existing thought, the inactive frames of VoIP streams are more suitable for data embedding than the active frames of the streams; that is, steganography in the inactive audio frames attains a largerData embedding capacity than that in the active audio frames under the same imperceptibility.
Abstract: This paper describes a novel high-capacity steganography algorithm for embedding data in the inactive frames of low bit rate audio streams encoded by G.723.1 source codec, which is used extensively in Voice over Internet Protocol (VoIP). This study reveals that, contrary to existing thought, the inactive frames of VoIP streams are more suitable for data embedding than the active frames of the streams; that is, steganography in the inactive audio frames attains a larger data embedding capacity than that in the active audio frames under the same imperceptibility. By analyzing the concealment of steganography in the inactive frames of low bit rate audio streams encoded by G.723.1 codec with 6.3 kb/s, the authors propose a new algorithm for steganography in different speech parameters of the inactive frames. Performance evaluation shows embedding data in various speech parameters led to different levels of concealment. An improved voice activity detection algorithm is suggested for detecting inactive audio frames taking into packet loss account. Experimental results show our proposed steganography algorithm not only achieved perfect imperceptibility but also gained a high data embedding rate up to 101 bits/frame, indicating that the data embedding capacity of the proposed algorithm is very much larger than those of previously suggested algorithms.

127 citations


01 Jan 2011
TL;DR: Different types of audio steganographic methods, advantages and disadvantages are discussed.
Abstract: Today’s large demand of internet applications requires data to be transmitted in a secure manner. Data transmission in public communication system is not secure because of interception and improper manipulation by eavesdropper. So the attractive solution for this problem is Steganography, which is the art and science of writing hidden messages in such a way that no one, apart from the sender and intend recipient, suspects the existence of the message, a form of security through obscurity. Audio steganography is the scheme of hiding the existence of secret information by concealing it into another medium such as audio file. In this paper we mainly discuss different types of audio steganographic methods, advantages and disadvantages. KEYWORD Steganography, Cryptography, Audio Steganography, LSB.

122 citations


Journal ArticleDOI
TL;DR: This paper targets the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in compressed video to hide data in natural sequences of multiple groups of pictures.
Abstract: This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video which operates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, we target the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional (B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on their associated macroblock prediction error, which is different from the approaches based on the motion vector attributes such as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieve robustness while maintaining a low prediction error level. The secret message bitstream is embedded in the least significant bit of both components of the candidate motion vectors. The method is implemented and tested for hiding data in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on two criteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Based on the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vector attribute-based method from the literature.

120 citations


Book ChapterDOI
18 May 2011
TL;DR: The goal of this paper is to subject this newly proposed steganographic algorithm HUGO to analysis, identify features capable of detecting payload embedded using such schemes and obtain a better picture regarding the benefit of adaptive steganography with public selection channels.
Abstract: Content-adaptive steganography constrains its embedding changes to those parts of covers that are difficult to model, such as textured or noisy regions. When combined with advanced coding techniques, adaptive steganographic methods can embed rather large payloads with low statistical detectability at least when measured using feature-based steganalyzers trained on a given cover source. The recently proposed steganographic algorithm HUGO is an example of this approach. The goal of this paper is to subject this newly proposed algorithm to analysis, identify features capable of detecting payload embedded using such schemes and obtain a better picture regarding the benefit of adaptive steganography with public selection channels. This work describes the technical details of our attack on HUGO as part of the BOSS challenge.

110 citations


Proceedings ArticleDOI
11 Jul 2011
TL;DR: Two ways are proposed to improve the conventional LSB modification technique by randomizing bit number of host message used for embedding secret message while the second way is to randomize sample number containing next secret message bit.
Abstract: Increased use of electronic communication has given birth to new ways of transmitting information securely. Audio steganography is the science of hiding some secret text or audio information in a host message. The host message before steganography and stego message after steganography have the same characteristics. Least Significant Bit (LSB) modification technique is the most simple and efficient technique used for audio steganography. The conventional LSB modification technique is vulnerable to steganalysis. This paper proposes two ways to improve the conventional LSB modification technique. The first way is to randomize bit number of host message used for embedding secret message while the second way is to randomize sample number containing next secret message bit. The improvised proposed technique works against steganalysis and decreases the probability of secret message being extracted by an intruder. Advanced Encryption Standard (AES) with 256 bits key length is used to secure secret message in case the steganography technique breaks. Proposed technique has been tested successfully on a. wav file at a sampling frequency of 8000 samples/second with each sample containing 8 bits.

107 citations


Proceedings ArticleDOI
25 Apr 2011
TL;DR: A current state of art literature in digital audio steganographic techniques is presented and their potentials and limitations to ensure secure communication are explored.
Abstract: Steganography has been proposed as a new alternative technique to enforce data security. Lately, novel and versatile audio steganographic methods have been proposed. A perfect audio Steganographic technique aim at embedding data in an imperceptible, robust and secure way and then extracting it by authorized people. Hence, up to date the main challenge in digital audio steganography is to obtain robust high capacity steganographic systems. Leaning towards designing a system that ensures high capacity or robustness and security of embedded data has led to great diversity in the existing steganographic techniques. In this paper, we present a current state of art literature in digital audio steganographic techniques. We explore their potentials and limitations to ensure secure communication. A comparison and an evaluation for the reviewed techniques is also presented in this paper.

94 citations


01 Jan 2011
TL;DR: A DCT based watermarking scheme is proposed which provides higher resistance to image processing attacks such as JPEG compression, noise, rotation, translation etc .
Abstract: Since all the multimedia products are released via internet so it’s an urgent need today to protect the data from malicious attacks. This lead to the research in the area of Digital watermarking which intends to protect the copyright information of the intellectuals. In this paper a DCT based watermarking scheme is proposed which provides higher resistance to image processing attacks such as JPEG compression, noise, rotation, translation etc .In this approach, the watermark is embedded in the mid frequency band of the DCT blocks carrying low frequency components and the high frequency sub band components remain unused. Watermark is inserted by adjusting the DCT coefficients of the image and by using the private key. Watermark can then be extracted using the same private key without resorting to the original image. Performance analysis shows that the watermark is robust.

88 citations


Journal ArticleDOI
TL;DR: This paper proposes a novel secret image sharing scheme by applying optimal pixel adjustment process to enhance the image quality under different payload capacity and various authentication bits conditions and demonstrates the efficacy of authentication capability of the proposed scheme.

Journal ArticleDOI
TL;DR: The experimental results show that the proposed method outperforms three recently published works, namely Mielikainen's, Zhang and Wang's, and Yang et al.'s methods.
Abstract: Recently, Zhang and Wang proposed a steganographic scheme by exploiting modification direction (EMD) to embed one secret digit d in the base-(2xn+1) notational system into a group of n cover pixels at a time. Therefore, the hiding capacity of the EMD method is log"2(2xn+1)/n bit per pixel (bpp). In addition, its visual quality is not optimal. To overcome the drawbacks of the EMD method, we propose a novel steganographic scheme by exploiting eight modification directions to hide several secret bits into a cover pixel pair at a time. By this way, the proposed method can achieve various hiding capacities of 1, 2, 3, 4, and 4.5 bpp and good visual qualities of 52.39, 46.75, 40.83, 34.83, and 31.70dB, respectively. The experimental results show that the proposed method outperforms three recently published works, namely Mielikainen's, Zhang and Wang's, and Yang et al.'s methods.

Journal ArticleDOI
TL;DR: This paper first analyzes the common limitations of the original PVD and its modified versions, and then proposes a more secure steganography based on a content adaptive scheme that achieves much better security compared with the previous PVD-based methods.
Abstract: Pixel-value differencing (PVD) based steganography is one of popular approaches for secret data hiding in the spatial domain. However, based on extensive experiments, we find that some statistical artifacts will be inevitably introduced even with a low embedding capacity in most existing PVD-based algorithms. In this paper, we first analyze the common limitations of the original PVD and its modified versions, and then propose a more secure steganography based on a content adaptive scheme. In our method, a cover image is first partitioned into small squares. Each square is then rotated by a random degree of 0, 90, 180 or 270. The resulting image is then divided into non-overlapping embedding units with three consecutive pixels, and the middle one is used for data embedding. The number of embedded bits is dependent on the differences among the three pixels. To preserve the local statistical features, the sort order of the three pixel values will remain the same after data hiding. Furthermore, the new method can first use sharper edge regions for data hiding adaptively, while preserving other smoother regions by adjusting a parameter. The experimental results evaluated on a large image database show that our method achieves much better security compared with the previous PVD-based methods.

Journal Article
TL;DR: This paper provides a critical review of steganography as well as to analyze the characteristics of various cover media namely image, text, audio and video in respects of the fundamental concepts, the progress of Steganographic methods and the development of the corresponding steganalysis schemes.
Abstract: The staggering growth in communication technology and usage of public domain channels (i.e. Internet) has greatly facilitated transfer of data. However, such open communication channels have greater vulnerability to security threats causing unauthorized information access. Traditionally, encryption is used to realize the communication security. However, important information is not protected once decoded. Steganography is the art and science of communicating in a way which hides the existence of the communication. Important information is firstly hidden in a host data, such as digital image, text, video or audio, etc, and then transmitted secretly to the receiver. Steganalysis is another important topic in information hiding which is the art of detecting the presence of steganography. This paper provides a critical review of steganography as well as to analyze the characteristics of various cover media namely image, text, audio and video in respects of the fundamental concepts, the progress of steganographic methods and the development of the corresponding steganalysis schemes

Proceedings ArticleDOI
24 Aug 2011
TL;DR: Experimental results showed that the proposed Video Steganography algorithm can hide a same-size video in the host video without obvious distortion in theHost video.
Abstract: This paper proposes a novel Video Steganography which can hide an uncompressed secret video stream in a host video stream with almost the same size. Each frame of the secret video will be Non-uniform rectangular partitioned and the partitioned codes obtained can be an encrypted version of the original frame. These codes will be hidden in the Least 4 Significant Bits of each frames of the host video. Experimental results showed that this algorithm can hide a same-size video in the host video without obvious distortion in the host video.

Book ChapterDOI
18 May 2011
TL;DR: A new methodology for the steganalysis of digital images that first extracts features via applying a function to the image, constructing the k variate probability density function (PDF) estimates, and downsampling it by a suitable downsamplings algorithm.
Abstract: This paper presents a new methodology for the steganalysis of digital images In principle, the proposed method is applicable to any kind of steganography at any domain Special interest is put on the steganalysis of Highly Undetectable Steganography (HUGO) The proposed method first extracts features via applying a function to the image, constructing the k variate probability density function (PDF) estimates, and downsampling it by a suitable downsampling algorithm The extracted feature vectors are then further optimized in order to increase the detection performance and reduce the computational time Finally using a supervised classification algorithm such as SVM, steganalysis is performed The proposed method is capable of detecting BOSSRank image set with an accuracy of 85%

Journal ArticleDOI
01 Mar 2011
TL;DR: The paper presents a new steganographic method called RSTEG (retransmission steganography), which is intended for a broad class of protocols that utilises retransmission mechanisms, to not acknowledge a successfully received packet in order to intentionally invoke retransmissions.
Abstract: The paper presents a new steganographic method called RSTEG (retransmission steganography), which is intended for a broad class of protocols that utilises retransmission mechanisms. The main innovation of RSTEG is to not acknowledge a successfully received packet in order to intentionally invoke retransmission. The retransmitted packet carries a steganogram instead of user data in the payload field. RSTEG is presented in the broad context of network steganography, and the utilisation of RSTEG for TCP (transmission control protocol) retransmission mechanisms is described in detail. Simulation results are also presented with the main aim of measuring and comparing the steganographic bandwidth of the proposed method for different TCP retransmission mechanisms, as well as to determine the influence of RSTEG on the network retransmission level.

Posted Content
TL;DR: A new algorithm to hide data inside image using steganography technique that uses binary codes and pixels inside an image to maximize the storage of data inside the image is proposed.
Abstract: In this paper, the authors propose a new algorithm to hide data inside image using steganography technique. The proposed algorithm uses binary codes and pixels inside an image. The zipped file is used before it is converted to binary codes to maximize the storage of data inside the image. By applying the proposed algorithm, a system called Steganography Imaging System (SIS) is developed. The system is then tested to see the viability of the proposed algorithm. Various sizes of data are stored inside the images and the PSNR (Peak signal-to-noise ratio) is also captured for each of the images tested. Based on the PSNR value of each images, the stego image has a higher PSNR value. Hence this new steganography algorithm is very efficient to hide the data inside the image.

Book ChapterDOI
TL;DR: While this chapter provides a historical context for stego, the emphasis is on digital applications, focusing on hiding information in digital image or audio files.
Abstract: Steganography is the art of covered, or hidden, writing. The purpose of steganography is covert communication—to hide the existence of a message from a third party. Knowledge of steganography is of increasing importance to individuals in the law enforcement, intelligence, and military communities. This chapter provides a high-level introduction to methods and tools for both hiding information (steganography) and detecting hidden information (steganalysis). This chapter is technical, in that it uses many examples using the current tools of the trade, without delving into the deeper mathematics, although references are provided to some of the ongoing research in the field. While this chapter provides a historical context for stego, the emphasis is on digital applications, focusing on hiding information in digital image or audio files. Examples of software tools that employ steganography to hide data inside of other files as well as software to detect such hidden files will also be presented.

Proceedings ArticleDOI
R. Balaji, G. Naveen1
15 May 2011
TL;DR: When steganographed by this method, the probability of finding the hidden information by an attacker is lesser when compared to the normal method of hiding information frame-by-frame in a sequential manner and it reduces the computational time taken for the extraction process.
Abstract: It is very essential to transmit important data like banking and military information in a secure manner. Video Steganography is the process of hiding some secret information inside a video. The addition of this information to the video is not recognizable by the human eye as the change of a pixel color is negligible. This paper aims to provide an efficient and a secure method for video Steganography. The proposed method creates an index for the secret information and the index is placed in a frame of the video itself. With the help of this index, the frames containing the secret information are located. Hence, during the extraction process, instead of analyzing the entire video, the frames containing the secret data are analyzed with the help of index at the receiving end. When steganographed by this method, the probability of finding the hidden information by an attacker is lesser when compared to the normal method of hiding information frame-by-frame in a sequential manner. It also reduces the computational time taken for the extraction process.

Proceedings ArticleDOI
12 Dec 2011
TL;DR: This article reviews steganography and steganalysis based on digital image based on spatial domain and transform domain embedding methods and some new trend and problems faced are discussed.
Abstract: With the rapid development of steganography, steganalysis has advanced quickly. Battle between steganography and steganalysis has become an important issue in information security. Aiming at a commonly used cover media, i.e., digital image, this article reviews steganography and steganalysis based on digital image. Concept and principle of steganography and steganalysis are illustrated. Spatial domain and transform domain embedding methods are generalized. And the recent advances in steganalysis are recapitulated. Then the performance specification of image steganography is discussed. Finally some new trend and problems faced are also discussed.

01 Jan 2011
TL;DR: Simulation results reveal that the novel scheme outperforms adaptive steganography technique based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.
Abstract: This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.

Posted Content
TL;DR: The different approaches towards implementation of steganography are elucidated using 'multimedia' file (text, static image, audio and video) and Network IP datagram as cover and some methods of Steganalysis will be discussed.
Abstract: Steganography is the technique of hiding confidential information within any media. Steganography is often confused with cryptography because the two are similar in the way that they both are used to protect confidential information. The difference between the two is in the appearance in the processed output; the output of steganography operation is not apparently visible but in cryptography the output is scrambled so that it can draw attention. Steganlysis is process to detect of presence of steganography. In this article we have tried to elucidate the different approaches towards implementation of steganography using 'multimedia' file (text, static image, audio and video) and Network IP datagram as cover. Also some methods of steganalysis will be discussed.

Proceedings ArticleDOI
29 Sep 2011
TL;DR: The approach is superior to a previous well-known steganalysis algorithm; the method remarkably improves the detection accuracy especially in the detection of the YASS steganograms that are produced with a large B-block size, which was not well addressed before.
Abstract: Recently well-designed adaptive steganographic systems, including ±1 embedding in the DCT domain with optimized costs to achieve the minimal-distortion [8], have posed serious challenges to steganalyzers. Additionally, although the steganalysis of Yet Another Steganographic Scheme (YASS) was actively conducted, the detection of the YASS steganograms by a large B-block parameter has not been well explored. In this paper, we aim to detect the state-of-the-art adaptive steganographic system in DCT-embedding and to improve the steganalysis of YASS. To detect DCT-embedding based adaptive steganography, we design the features of differential neighboring joint density on the absolute array of DCT coefficients between the original JPEG images and the calibrated versions. To discriminate YASS steganograms from covers, the candidate blocks that are possibly used for embedding and the non-candidate block neighbors that are impossibly used for information hiding are identified first. The difference of the neighboring joint density between candidate blocks and the non-candidate neighbors is obtained. Support Vector Machine (SVM) and logistic regression classifiers are employed for classification. Experimental results show that our approach is very promising when detecting DCT-embedding based adaptive steganography. Compared to the steganalysis based on CC-PEV feature set, our method greatly improves the detection accuracy; the advantage is especially noticeable in the detection of the steganograms with low relative payload. In steganalysis of YASS, our approach is superior to a previous well-known steganalysis algorithm; our method remarkably improves the detection accuracy especially in the detection of the YASS steganograms that are produced with a large B-block size, which was not well addressed before.

Journal ArticleDOI
TL;DR: A new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images is proposed and prominently outperforms the well-known Markov-process based approach.
Abstract: The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.

Journal ArticleDOI
TL;DR: A novel polynomial-based secret image sharing scheme with two achievements is proposed so that the block size is determined dynamically according to the size of hidden data and therefore, all the capacity of cover images is used for data hiding.

Journal ArticleDOI
TL;DR: By adopting an object oriented steganography mechanism, in the sense that, the authors track skin tone objects in image, they get a higher security and simulation result shows that satisfactory PSNR (Peak-Signal-to-Noise Ratio) is also obtained.
Abstract: Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement Steganography is skin tone region of images (1). Here secret data is embedded within skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed using frequency domain approach - DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that sub-band. For data hiding two cases are considered, first is with cropping and other is without cropping. In both the cases different steps of data hiding are applied either by cropping an image interactively or without cropping i.e. on whole image. Both cases are compared and analyzed from different aspects. This is concluded that both cases offer enough security. Main feature of with cropping case is that this results into an enhanced security because cropped region works as a key at decoding side. Where as without cropping case uses embedding algorithm that preserves histogram of DWT coefficient after data embedding also by preventing histogram based attacks and leading to a more security. This study shows that by adopting an object oriented steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And simulation result shows that satisfactory PSNR (Peak-Signal-to-Noise Ratio) is also obtained.

Journal ArticleDOI
TL;DR: How difficult it is for Eve to detect the presence of secret messages is analyzed, and rates of steganog raphic communication and secret key consumption for certain protocols are estimated.
Abstract: Steganography is the process of hiding secret information by embedding it in an "innocent" message. We present protocols for hiding quantum information in a codeword of a quantum error-correcting code passing through a channel. Using either a shared classical secret key or shared entanglement the sender (Alice) disguises her information as errors in the channel. The receiver (Bob) can retrieve the hidden information, but an eavesdropper (Eve) with the power to monitor the channel, but without the secret key, cannot distinguish the message from channel noise. We analyze how difficult it is for Eve to detect the presence of secret messages, and estimate rates of steganographic communication and secret key consumption for certain protocols.

Journal ArticleDOI
TL;DR: Methods and algorithms used to embed messages in images and to detect embedded messages are explored and a demonstration of how to embed hidden information in an image using available steganography tools is provided.
Abstract: Image steganography is used to embed covert messages in the form of files, text, or other images in digital images. The intent is to transmit hidden information. Steganalysis is the process used to detect hidden messages in images. Although steganography is not a new discipline, it has become increasingly important in today's digital world where information is often and easily exchanged through the Internet, email, and other means using computers. The need for better methods and techniques which can be used both to embed hidden information in images and to detect messages hidden in images is driving new research in the area of steganography and steganalysis. This article surveys image steganography and steganalysis. The aim is to introduce the uninformed reader to image steganography and steganalysis. The key concepts behind image steganography and steganalysis are explained. The history and origin of steganography are outlined. Steganography is compared with watermarking in technique and intent. Details of how images are represented are explained. Methods and algorithms used to embed messages in images and to detect embedded messages are explored. Currently available steganography and steganalysis tools are explained. A demonstration of how to embed hidden information in an image using available steganography tools is provided. WIREs Comp Stat 2011 3 251–259 DOI: 10.1002/wics.152 For further resources related to this article, please visit the WIREs website.

Journal Article
TL;DR: This paper starts by describing the main existing methods and techniques in steganography that allow us to hide the existence of a message, together with the mostly used steganalysis techniques to counter them.
Abstract: Our main goal in this paper is to give new insights and directions on how to improve existing methods of hiding secret messages, possibly by combining steganography and cryptography. We start by describing the main existing methods and techniques in steganography that allow us to hide the existence of a message, together with the mostly used steganalysis techniques to counter them. We then illustrate two different approaches that help us achieve a higher level of secrecy and security, together with their limitations. The first method is about combining steganography and cryptography in such a way to make it harder for a steganalyst to retrieve the plaintext of a secret message from a stego-object if cryptanalysis were not used. The second method does not use any cryptographic techniques at all and relies solely on steganographic ones.