جستجوی مقالات مرتبط با کلیدواژه "steganography" در نشریات گروه "پدافند غیرعامل"

The ever-increasing development of telecommunications has made secure transmission one of the most important issues today. Since there is a high hiding capacity in the image, the use of image encryption is much more common than other methods of encryption. This article uses the covert imaging technique with the wavelet transform method, and the results show that this method has high resistance. For the analysis of hidden images, an algorithmic wavelet transform method using matrix features (GLCM) and co-occurrence vectors (DCL) is presented. After checking these values in the original and cover images, the different features between these images are extracted and used to train the multilayer neural network (MLP). The classification stage has been performed using the layers of this neural network and the proposed algorithm has been tested for a database of 200 standard images (Casia-Iris). The detection accuracy of 90% of the hidden images in the proposed method shows the superiority of this hidden mining method over other methods.

Today, Voice over Internet Protocol (VoIP) is widely used in real-time communication and social networks and has become a suitable carrier for steganography methods. To confronting these threats, many steganalysis methods have been invented, among the proposed solutions, the combination of signal processing and machine learning methods has made it possible to create steganalysis methods with high accuracy. In this paper, a combined approach of speech signal processing methods and artificial intelligence algorithms is used. In this research, first, data pre-processing is done on compressed audio signal with G.729 codec, which extracts intra-frame features and inter-frame correlations with good resolution. Then the obtained results are given to a deep learning network to train cover data from stego data. The results of the implementation include the improvement in both the detection accuracy and the computation time. This method has been analyzed for two important steganography families, QIM and PMS, and the proposed method has been tested and implemented for different embedding rates. Another important point is the real-time test of the presented method, which for 1000 millisecond files, the response time was less than 5 millisecond, which shows the high speed of the proposed model in the execution phase.

One of the most important information security techniques is the hiding of information. Steganography is the art and science of hiding information in the cover of data (in the form of text, image, video, or audio) such that it does not arise any suspicions, and is difficult or even impossible to discover. This paper presents a method for steganography in the form of text which uses the methods of text justifying in typing editors. The method presented in this paper is able to hide information better than some of previous algorithms in this field. This algorithm is resistant to various forms of attack such as visual, structural and statistical attacks. Another important capability of this method is that it can be used to send printed information.

Adaptive steganography methods use variable embedding capacity according to the uniformity or edges of image areas. ALSBMR is an adaptive method with two main stages: Selecting suitable pixels, and embedding them using the LSBMR method. This method utilizes two adaptive keys between the sender and the receiver to determine the block rotation angle and select the embedding path. In the original method, the keys are randomly selected by the sender with no specific criteria and then sent to the receiver. The proposed method models key selection as an optimization problem and uses Genetic Algorithm (GA) and Teaching-Learning-Based Optimization (TLBO) to find the optimal keys. Two fitness functions are used to further evaluate the difference as well as the histogram difference between the cover and stego images. The results show that the image embedded with the proposed method has improved quality and security compared to the base method. Since all steganography methods require embedding keys, intelligent key selection can improve the performance of existing steganography methods.

The popularity of audio formats usually attracts the attention of intruders and criminals to use this medium as a cover for establishing their secret communications. The extensive use of this formats, along with  various modern techniques, designed for audio steganography, can cause the cyber spaces to be insecure environments. In order to deal with threats, some audio steganalysis techniques have been presented that statistically analyze various audio formats, such as music, MP3, and VoIP, efficiently. Among the presented approaches, combining the techniques of signal processing and machine learning has made possible the creation of steganalyzers that are highly accurate. However, since the statistical properties of audio files differ from purely speech ones, the current steganalysis methods cannot detect speech stego files,            accurately. Another issue is the large number of analysis dimensions which increase the implementation cost, significantly. As response to these issues, this paper proposes the percentage of equal adjacent  samples (PEAS) feature, as a one-dimensional feature for speech steganalysis. Using a classifier, based on the Gaussian membership function, on stego instances with 50% embedding ratio, the evaluation results for the designed steganalyzer, show a sensitivity of 99.82%. Additionally, it can efficiently estimate the length of a hidden message with the desirable accuracy. Also, the PEAS steganalysis was evaluated on a database, containing classic music instances, and the results show an 81.2% efficient performance.

Steganography on the VoIP Platform is a Real-time network Steganography Method, It uses VoIP Protocols as a cover channel to hide messages. At the moment, real-time steganography is one of the challenging research areas. The reason for this is the inherent characteristics of the transfer of information in real time, which limits the complexity of operations. In this paper, we first describe the definition of Steganography and describe the VoIP concept briefly, and then we will describe some of the work done in this field. In continuation, we express the way VoIP works and the problems that we face, and then we consider the concept of VoIP Steganography and describe its challenges. Finally, introduce some of the most widely used methods of VoIP Steganography, and then we compare the different ways of VoIP steganography with each other, and discuss the weaknesses and strengths of each of them. In the final section of this paper, we will give the research results.

In all of the image steganography algorithms, the embed capacity and resistance are considered. Hence, in this paper a new method is proposed that not only has high capacity for embed, but also has high resistance against steg analysis methods. This method presents an initiative to collate the confidential secret message with a matrix called K, thus large amount of information does not need to be transferred across the channel which effects security, resistance and embed capacity. After classification and mapping, the amount of information that is needed to be sent, changes to numbers between -7 and 7. Hence, by reducing the secret bits needed to send, the changes in the cover are also reduced and the result are optimized. B y comparing PSNR and SSIM  for this method with other methods in this area, we clearly see improvements in these parameters. The average of PSNR and SSIM is 44.36 and 0.97, respectively both of which are in the perfect range. The average of  numerical value of insertion is 5.99 bits per pixel, which  provides a very high capacity for insertion.

Statistical detectablity of a steganalyser declares its ability to distinguish between cover and stego images. Optimum steganographer must be designed to confuse the corresponding steganalysers in detecting stego images. Thus, designing a steganographic algorithm based on reducing statistical detectability is of great importance. Unfortunately establishing a perfect relation between pixel cost and statistical detectability is still an open problem. In this paper, we have modelled steganalyser by special graphical models, called topic models, to estimate the error rate of a steganalyser in terms of the steganographic pixel cost. Morover, we have redefined the steganographic capacity and pixel cost based on such models. It is also shown that the new critera are compatible with classical ones, like PSNR. Then, an algorithm is designed as per such criteria. It is shown empirically that the presented algorithm is comparable to the best analytically designed algorithms presented so far. It is worth mentioning that the paper is focused on establishing a mathematical basis for the relation between the steganalyzer error and pixel cost and not improving the current algorithms. Nonetheless, as compared to the rivals, a small improvement, about 0.5% in steganalysis error, has also been achieved.

Steganography is an information hiding application which aims to hide secret messages imperceptibly into commonly used media. In this paper, we describe an Optimal embedding method based on linear codes that conforms to least significant bit, that is, the secret data is embedded into a cover message by parity check matrix. The new method not only benefits from the field of location and detection and error correction bit stream received by the receiver, but also can increase the Resistance between 94% to 100%, the transparency (PSNR) up to 84/71, and the similarity (SSIM) up to % 9999/99.

Steganography is largely used in information security systems. In steganography, the goal is to hide the information such that only the sender and receiver are aware of the communication and information. Steganography is carried out on different media such as image, sound and text. It can be performed in both transform and spatial domains. In transform domain methods, first the cover image is transformed into a different domain using, for example, a frequency transform. Then, the secret message is embedded in the conversion coefficients. In this paper, a new steganography method based on the transform domain is proposed to conceal text in an HSV color image. After converting the image to HSV, it is divided into blocks and the mixed column transfer is applied on each block. The secret message is embedded in the transferred columns. Simulation results and comparisons show the high capacity and increased security of the proposed method.

The secure and hidden communication, is one of the essential requirements in cyberspace. Steganography is the best approach, in which the communication security in common network like Internet, is provided. Steganography based on Mod 4 is one of the prevalent methods in DCT transformation domain which embeds the message in the image in different way from the past methods. In this method, correlation between coefficients is kept well and is robust against blind steganalysis .
The ultimate of this paper is to improve steganography based on Mod 4 , that is, increasing capacity and transparency, as two principal criteria in steganography. In the proposed method, by changing the size of GQC blocks and by determining the suitable conditions for choosing the valid blocks, transparency is increased more than 5 percent and capacity is increased more than 86 percent .
For evaluation the stego images transparency in the proposed method, and comparing with basic Mod4 method, two criteria, PSNR and SSIM are used .

In the information hiding systems, three main elements of capacity, security and resistance are involved. Security and emplacement capacity have a special position in steganography and the steganography methods emphasize the reduction of changes caused by emplacement. But one of the very important issues in passive defense is the appropriate application of the existing memory space for steganography during crisis. This paper attempts to present a dynamic method to implement image steganography by considering minimum factors in the image properties for critical conditions. The consumed memory space and the similarity of image are two parameters that are dealt with in this paper. In this paper, at first, a compressed image of the main image is obtained using the Takin Value Analysis Method and then the steganography is performed. In this method, steganography in the compressed image supplement is done and later, will be accumulated with the compressed image itself and the ultimate image will be obtained which resembles the main image and this similarity value depends on the value of the special parameter in the computational process called K(special value of matrix). The lower the value of K, the lower the similarity to the main image but a higher memory space will be available for steganography.

In the present paper, a new adaptive steganographic method has been proposed to embed secret data in an image. Proposed method allows the user to set algorithm parameters in a way that reducing the distortion resulted from stegnanography in the image in spite of embedding secret data in all pixels of image, considering size of secret data required for sending to destination. These parameters can be also used as a secret steganographic key. In this method, considering the human vision system, more secrete data bits are embedded in edged areas of image than in its smooth ones. In the proposed method, blocks are classified into different groups based on the extent of local complexity of pixels, and the capacity of each group is determined by total size of secret data. This method is also resistant to statistical and histogram steganalysis attack.