11 Pages, 5 Figures; International Journal of Computers, Information Technology and Engineering (IJCITAE), Vol. 2, No 1, June, 2008, Serial Publications
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.
For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of Rich Models (RM), followed by classification using an Ensemble Classifier (EC). In 2015, the first study using a convolutional neural network (CNN) obtained the first results of steganalysis by Deep Learning approaching the performances of the two-step approach (EC + RM). Between 2015-2018, numerous publications have shown that it is possible to obtain improved performances, notably in spatial steganalysis, JPEG steganalysis, Selection-Channel-Aware steganalysis, and in quantita…
Three-dimensional (3-D) meshes are commonly used to represent virtual surfaces and volumes. Over the past decade, 3-D meshes have emerged in industrial, medical, and entertainment applications, being of large practical significance for 3-D mesh steganography and steganalysis. In this article, we provide a systematic survey of the literature on 3-D mesh steganography and steganalysis. Compared with an earlier survey [1], we propose a new taxonomy of steganographic algorithms with four categories: 1) two-state domain, 2) LSB domain, 3) permutation domain, and 4) transform domain. Regarding stega…
The aim of the steganography methods is to communicate securely in a completely undetectable manner. LSB Matching and LSB Matching Revisited steganography methods are two general and esiest methods to achieve this aim. Being secured against first order steganalysis methods is the most important feature of these methods. On the other hand, these methods don't consider inter pixel dependency. Therefore, recently, several steganalysis methods are proposed that by using co-occurrence matrix detect stego images that are hidden by these steganography methods. Therefore, if incremental and decrementa…
Steganalysis means analysis of stego images. Like cryptanalysis, steganalysis is used to detect messages often encrypted using secret key from stego images produced by steganography techniques. Recently lots of new and improved steganography techniques are developed and proposed by researchers which require robust steganalysis techniques to detect the stego images having minimum false alarm rate. This paper discusses about the different Steganalysis techniques and help to understand how, where and when this techniques can be used based on different situations.
This paper presents a survey of text steganography methods used for hid- ing secret information inside some covertext. Widely known hiding techniques (such as translation based steganography, text generating and syntactic embed- ding) and detection are considered. It is shown that statistical analysis has an important role in text steganalysis.
Retention of secrecy is one of the significant features during communication activity. Steganography is one of the popular methods to achieve secret communication between sender and receiver by hiding message in any form of cover media such as an audio, video, text, images etc. Least significant bit encoding is the simplest encoding method used by many steganography programs to hide secret message in 24bit, 8bit colour images and grayscale images. Steganalysis is a method of detecting secret message hidden in a cover media using steganography. RS steganalysis is one of the most reliable stegan…
Steganalysis has been an important research topic in cybersecurity that helps to identify covert attacks in public network. With the rapid development of natural language processing technology in the past two years, coverless steganography has been greatly developed. Previous text steganalysis methods have shown unsatisfactory results on this new steganography technique and remain an unsolved challenge. Different from all previous text steganalysis methods, in this paper, we propose a text steganalysis method(TS-CNN) based on semantic analysis, which uses convolutional neural network(CNN) to e…
Steganography is the art and science of writing hidden messages in such a way that no one apart from the intended recipient knows of the existence of the message. In today's world, it is widely used in order to secure the information. In this paper, the traditional spectral estimation methods are introduced. The performance analysis of each method is examined by comparing all of the spectral estimation methods. Finally, from utilizing those performance analyses, a brief pros and cons of the spectral estimation methods are given. Also we give a steganography demo by hiding information into a so…
Any Universal Steganalysis algorithm developed should be tested with various stego-images to prove its efficiency. This work is aimed to build the stego-image database which is obtained by implementing various RGB Least Significant Bit Steganographic algorithms. Though there are many stego-images sources available on the internet it lacks in the information such as how many rows has been infected by the steganography algorithms, how many bits have been modified and which channel has been affected. These parameters are important for Steganalysis algorithms and it helps to rate its efficiency. I…
TranSteg (Trancoding Steganography) is a fairly new IP telephony steganographic method that functions by compressing overt (voice) data to make space for the steganogram by means of transcoding. It offers high steganographic bandwidth, retains good voice quality and is generally harder to detect than other existing VoIP steganographic methods. In TranSteg, after the steganogram reaches the receiver, the hidden information is extracted and the speech data is practically restored to what was originally sent. This is a huge advantage compared with other existing VoIP steganographic methods, where…
Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with "laboratory" data. However, applying machine-learning classification to a new source of data is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the true steganographic algorithms…
Recent work (Baluja, 2017) showed that using a pair of deep encoders and decoders, embedding a full-size secret image into a container image of the same size is achieved. This method distributes the information of the secret image across all color channels of the cover image, thereby, it is difficult to discover the secret image using conventional methods. In this paper, we propose a new steganalysis technique which achieves complete recovery of the embedded secret in steganography images. We incorporate a deep neural network to decode an approximate estimate of the secret image followed by a …
Targeted Pooled Latent-Space Steganalysis Applied to Generative Steganography, with a Fix
Univ. Lille, CNRS, Centrale Lille（里尔大学、法国国家科学研究中心、中央里尔学院）；Artificial Intelligence Center Czech Technical University Prague（捷克布拉格理工大学人工智能中心）；LIST3N, UTT University of Techn. of Troyes（特鲁瓦技术大学 LIST3N）
This article describes novel text steganalysis method. The archiver "Bzip2" used for detection stegotext generated by Texto stegosystem. Experiments show that proposed approach gets better performance than typical existing methods. The detection accuracy exceeds 99.98% for text segments with size 400 bytes.
Since the emergence of deep learning and its adoption in steganalysis fields, most of the reference articles kept using small to medium size CNN, and learn them on relatively small databases. Therefore, benchmarks and comparisons between different deep learning-based steganalysis algorithms, more precisely CNNs, are thus made on small to medium databases. This is performed without knowing: 1. if the ranking, with a criterion such as accuracy, is always the same when the database is larger, 2. if the efficiency of CNNs will collapse or not if the training database is a multiple of magnitude lar…
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions
University of Medea（迈迪亚大学）；University of Sciences and Technology Houari Boumediene (USTHB)（侯阿里·布迈丁科技大学）；University of Dubai（迪拜大学）；Universitat Oberta de Catalunya (UOC)（加泰罗尼亚开放大学）；University of Sharjah（沙迦大学）；De Montfort University（德蒙福特大学）
Steganography is the science of hiding and communicating a secret message by embedding it in an innocent looking text such that the eavesdropper is unaware of its existence. Previously, attempts were made to establish steganography using quantum key distribution (QKD). Recently, it has been shown that such protocols are vulnerable to a certain steganalysis attack that can detect the presence of the hidden message and suppress the entire communication. In this work, we elaborate on the vulnerabilities of the original protocol which make it insecure against this detection attack. Further, we pro…
Steganalysis tools play an important part in saving time and providing new angles of attack for forensic analysts. StegExpose is a solution designed for use in the real world, and is able to analyse images for LSB steganography in bulk using proven attacks in a time efficient manner. When steganalytic methods are combined intelligently, they are able generate even more accurate results. This is the prime focus of StegExpose.
Steganography is about how to send secret message covertly. And the purpose of steganalysis is to not only detect the existence of the hidden message but also extract it. So far there have been many reliable detecting methods on various steganographic algorithms, while there are few approaches that can extract the hidden information. In this paper, the difficulty of extracting hidden information, which is essentially a kind of privacy, is analyzed with information-theoretic method in the terms of unicity distance of steganographic key (abbreviated stego key). A lower bound for the unicity dist…
Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and transactions, attacking algorithms against steganography, called steganalysis, exist. Currently, there are many studies implementing deep learning to the steganography algorithm. However, conventional steganalysis is no longer effective for deep learning based steganography algorit…
The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpret the concealed information inside digital medium such as image, audio and video. There are strong indications that hiding information inside digital medium has been used for planning criminal activities. In this way, it is important to develop a steganalysis technique which detects the existence of hidden messages inside digital medium. This paper focuses on universal image steganalysis method which uses RGB to HSI colour model conversion. Any Universal Steganalysis algorithm developed should be …
Seoul National University（首尔大学）；Ewha Womans University（梨花女子大学）；Kangwon National University（江原大学）；Ziovision
Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original image…
Traditional image steganography modifies the content of the image more or less, it is hard to resist the detection of image steganalysis tools. To address this problem, a novel method named generative coverless information hiding method based on generative adversarial networks is proposed in this paper. The main idea of the method is that the class label of generative adversarial networks is replaced with the secret information as a driver to generate hidden image directly, and then extract the secret information from the hidden image through the discriminator. It's the first time that the cov…
This paper presents two novel approaches to increase performance bounds of image steganography under the criteria of minimizing distortion. First, in order to efficiently use the images' capacities, we propose using parallel images in the embedding stage. The result is then used to prove sub-optimality of the message distribution technique used by all cost based algorithms including HUGO, S-UNIWARD, and HILL. Second, a new distribution approach is presented to further improve the security of these algorithms. Experiments show that this distribution method avoids embedding in smooth regions and…
Redundant information of low-bit-rate speech is extremely small, thus it's very difficult to implement large capacity steganography on the low-bit-rate speech. Based on multiple vector quantization characteristics of the Line Spectrum Pair (LSP) of the speech codec, this paper proposes a steganography scheme using a 3D-Magic matrix to enlarge capacity and improve quality of speech. A cyclically moving algorithm to construct a 3D-Magic matrix for steganography is proposed in this paper, as well as an embedding and an extracting algorithm of steganography based on the 3D-Magic matrix in low-bit-…
FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis
International School, Beijing University of Posts and Telecommunications（北京邮电大学国际学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络安全学院）
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）
Image steganography is the art and science of using images as cover for covert communications. With the development of neural networks, traditional image steganography is more likely to be detected by deep learning-based steganalysis. To improve upon this, we propose image steganography network based on style transfer, and the embedding of secret messages can be disguised as image stylization. We embed secret information while transforming the content image style. In latent space, the secret information is integrated into the latent representation of the cover image to generate the stego image…
Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent spa…
We present here the first work to propose different mechanisms for hiding data in the Extensible Messaging and Presence Protocol (XMPP). This is a very popular instant messaging protocol used by many messaging platforms such as Google Talk, Cisco, LiveJournal and many others. Our paper describes how to send a secret message from one XMPP client to another, without raising the suspicion of any intermediaries. The methods described primarily focus on using the underlying protocol as a means for steganography, unlike other related works that try to hide data in the content of instant messages. In…
TSCL:Multi-party loss Balancing scheme for deep learning Image steganography based on Curriculum learning
Qianan College, North China University of Science and Technology（华北科技大学千安学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络安全学院）；College of Science, North China University of Science and Technology（华北科技大学理学院）
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 approac…
The paper concerns available steganographic techniques that can be used for creating covert channels for VoIP (Voice over Internet Protocol) streams. Apart from characterizing existing steganographic methods we provide new insights by presenting two new techniques. The first one is network steganography solution which exploits free/unused protocols' fields and is known for IP, UDP or TCP protocols but has never been applied to RTP (Real-Time Transport Protocol) and RTCP (Real-Time Control Protocol) which are characteristic for VoIP. The second method, called LACK (Lost Audio Packets Steganogra…
Digital image steganalysis, or the detection of image steganography, has been studied in depth for years and is driven by Advanced Persistent Threat (APT) groups', such as APT37 Reaper, utilization of steganographic techniques to transmit additional malware to perform further post-exploitation activity on a compromised host. However, many steganalysis algorithms are constrained to work with only a subset of all possible images in the wild or are known to produce a high false positive rate. This results in blocking any suspected image being an unreasonable policy. A more feasible policy is to f…
University of Edinburgh（爱丁堡大学）；Tsinghua University（清华大学）
Image steganography refers to the process of hiding information inside images. Steganalysis is the process of detecting a steganographic image. We introduce a steganalysis approach that uses an ensemble color space model to obtain a weighted concatenated feature activation map. The concatenated map helps to obtain certain features explicit to each color space. We use a levy-flight grey wolf optimization strategy to reduce the number of features selected in the map. We then use these features to classify the image into one of two classes: whether the given image has secret information stored or…
In this paper, a novel steganographic scheme based on chaotic iterations is proposed. This research work takes place into the information hiding security framework. The applications for anonymity and privacy through the Internet are regarded too. To guarantee such an anonymity, it should be possible to set up a secret communication channel into a web page, being both secure and robust. To achieve this goal, we propose an information hiding scheme being stego-secure, which is the highest level of security in a well defined and studied category of attacks called "watermark-only attack". This cat…
Deep image steganography is a data hiding technology that conceal data in digital images via deep neural networks. However, existing deep image steganography methods only consider the visual similarity of container images to host images, and neglect the statistical security (stealthiness) of container images. Besides, they usually hides data limited to image type and thus relax the constraint of lossless extraction. In this paper, we address the above issues in a unified manner, and propose deep image steganography that can embed data with arbitrary types into images for secure data hiding and…
Coverless steganography has been a great interest in recent years, since it is a technology that can absolutely resist the detection of steganalysis by not modifying the carriers. However, most existing coverless steganography algorithms select images as carriers, and few studies are reported on coverless video steganography. In fact, video is a securer and more informative carrier. In this paper, a novel coverless video steganography algorithm based on maximum Direct Current (DC) coefficients is proposed. Firstly, a Gaussian distribution model of DC coefficients considering video coding proce…
We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image classification and adversarial attacks to embed secret information within images. Thus, we use the attacks to embed an encoding of the message within images and the related deep neural network outputs to extract it. The key properties of adversarial attacks (invisible perturbations, nontransferability, resilience to tampering) offer guarantees regarding the confidentiality and the integrity of the hidd…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Exp…
In this article, one of the most important methods of steganography on VoIP called echo hiding is improved. This method has advantages in maintaining the statistical and perceptual characteristics of audio signals as well as security against the sensitivity of the human audio system (HAS). However, it has lots of errors in detecting coded and hidden messages, which is detectable using existing steganalysis methods. The percentage of extracting messages in these improved methods of echo hiding is high, but they lower the security of the method. In this article, a method is presented to improve …
Steganography and steganalysis are two important branches of the information hiding field of research. Steganography methods consist in hiding information in such a way that the secret message is undetectable for the uninitiated. Steganalyzis encompasses all the techniques that attempt to detect the presence of such hidden information. This latter is usually designed by making classifiers able to separate innocent images from steganographied ones according to their differences on well-selected features. We wonder, in this article whether it is possible to construct a kind of universal steganal…
CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning
Qianan College, North China University of Science and Technology（华北科技大学千安学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络安全学院）；College of Science, North China University of Science and Technology（华北科技大学理学院）
Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Different applications have different requirements of the steganography technique used. For example, some applications may require absolute invisibility o…
In today's world the art of sending & displaying the hidden information especially in public places, has received more attention and faced many challenges. Therefore, different methods have been proposed so far for hiding information in different cover media. In this paper a method for hiding of information on the billboard display is presented. It is well known that encryption provides secure channels for communicating entities. However, due to lack of covertness on these channels, an eavesdropper can identify encrypted streams through statistical tests and capture them for further cryptanaly…
Steganography and Cryptography are two popular ways of sending vital information in a secret way. One hides the existence of the message and the other distorts the message itself. There are many cryptography techniques available; among them AES is one of the most powerful techniques. In Steganography we have various techniques in different domains like spatial domain, frequency domain etc. to hide the message. It is very difficult to detect hidden message in frequency domain and for this domain we use various transformations like DCT, FFT and Wavelets etc. In this project we are developing a s…
