Steganography is the discipline that deals with concealing the existence of secret communications. Existing research already provided several fundamentals for defining steganography and presented a multitude of hiding methods and countermeasures for this research discipline. We identified that no work exists that discusses the process of applying steganography from an individual's perspective. This paper presents a phase model that explains pre-conditions of applying steganography as well as the decision-making process and the final termination of a steganographic communication. The model can be used to explain whether an individual can use steganography and to explain whether and why an individual desires to use steganography. Moreover, the model can be used in research publications to indicate the addressed model's phase of scientific contributions. Furthermore, our model can be used to teach the process of steganography-application to students.
Simmons introduced his so-called that provides the fundamental scenario in which steganography is applied @cite_2 . In his scenario, two prisoners try to escape jail. For a successful escape, they need to work together. However, they cannot directly exchange messages. Instead, the only way to exchange messages is to hand over all messages to a so-called that can read and modify the messages. The prisoners need to apply steganography so that they can plan their escape without letting the warden notice. Simmons describes a generalized case of steganography-application that comprises two important elements that we discuss in our work: the reason that leads to the application of steganography as well as the decision-making process of the prisoners. Modifications of the Prisoner's Problem exist, e.g. @cite_27 .
@cite_27 @cite_2
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We want to enable a pair of communicating users to exchange secret messages while hiding the fact that secret communication is taking place. We propose a linguistic steganography approach, where each human message is hidden in another human-like message. A hard open question is how to keep the steganographic message small -- existing related tools tend to blow up its size, thereby revealing the use of steganography. We encrypt by compressing each message, mapping it to a plausible sequence of words (using a language model), and letting the human user edit the outcome to produce a human-like message; we decrypt with a Viterbi-like state decoder. Our approach aims in producing text that a human can edit and fix with minimal effort. As a first step, we build a prototype of our system that helps users encrypt English messages (into English messages), and we report on first experiments on Mechanical Turk. Steganography aims to conceal the very fact that the communication takes place, by embedding a message into a digit object such as image without introducing noticeable artifacts. A number of steganographic systems have been developed in past years, most of which, however, are confined to the laboratory conditions where the real-world use of steganography are rarely concerned. In this paper, we introduce an alternative perspective to steganography. A graph-theoretic model to steganography on social networks is presented to analyze real-world steganographic scenarios. In the graph, steganographic participants are corresponding to the vertices with meaningless unique identifiers. Each edge allows the two vertices to communicate with each other by any steganographic algorithm. Meanwhile, the edges are associated with weights to quantize the corresponding communication risk (or say cost). The optimization task is to minimize the overall risk, which is modeled as additive over the social network. We analyze different scenarios on a social network, and provide the suited solutions to the corresponding optimization tasks. We prove that a multiplicative probabilistic graph is equivalent to an additive weighted graph. From the viewpoint of an attacker, he may hope to detect suspicious communication channels, the data encoder(s) and the data decoder(s). We present limited detection analysis to steganographic communication on a network. Two accomplices in a crime have been arrested and are about to be locked in widely separated cells. Their only means of communication after they are locked up will he by way of messages conveyed for them by trustees -- who are known to be agents of the warden. The warden is willing to allow the prisoners to exchange messages in the hope that he can deceive at least one of them into accepting as a genuine communication from the other either a fraudulent message created by the warden himself or else a modification by him of a genuine message. However, since he has every reason to suspect that the prisoners want to coordinate an escape plan, the warden will only permit the exchanges to occur if the information contained in the messages is completely open to him -- and presumably innocuous. The prisoners, on the other hand, are willing to accept these conditions, i.e., to accept some risk of deception in order to be able to communicate at all, since they need to coordinate their plans. To do this they will have to deceive the warden by finding a way of communicating secretly in the exchanges, i.e., of establishing a “subliminal channel” between them in full view of the warden, even though the messages themselves contain no secret (to the warden) information‡. Since they anticipate that the warden will try to deceive them by introducing fraudulent messages, they will only exchange messages if they are permitted to authenticate them. 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. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.