List of Articles Steganography

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  1 - Cover Selection Steganography Via Run Length Matrix and Human Visual System
  Sara Nazari Mohammad Shahram Moin
  10.7508/jist.2013.02.007
  A novel approach for steganography cover selection is proposed, based on image texture features and human visual system. Our proposed algorithm employs run length matrix to select a set of appropriate images from an image database and creates their stego version after e More

  A novel approach for steganography cover selection is proposed, based on image texture features and human visual system. Our proposed algorithm employs run length matrix to select a set of appropriate images from an image database and creates their stego version after embedding process. Then, it computes similarity between original images and their stego versions by using structural similarity as image quality metric to select, as the best cover, one image with maximum similarity with its stego. According to the results of comparing our new proposed cover selection algorithm with other steganography methods, it is confirmed that the proposed algorithm is able to increase the stego quality. We also evaluated the robustness of our algorithm over steganalysis methods such as Wavelet based and Block based steganalyses; the experimental results show that the proposed approach decreases the risk of message hiding detection. Manuscript profile
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  2 - Active Steganalysis of Transform Domain Steganography Based on Sparse Component Analysis
  Hamed Modaghegh Seyed Alireza  Seyedin
  10.7508/jist.2015.02.002
  This paper presents a new active steganalysis method to break the transform domain steganography. Most of steganalysis techniques focus on detecting the presence or absence of a secret message in a cover (passive steganalysis), but in some cases we need to extract or es More

  This paper presents a new active steganalysis method to break the transform domain steganography. Most of steganalysis techniques focus on detecting the presence or absence of a secret message in a cover (passive steganalysis), but in some cases we need to extract or estimate hidden message (active steganalysis). Although estimating the message is important but there is little research in this area. A new active steganalysis method which is based on Spars Component Analysis (SCA) technique is presented in this work. Here, the sparsity property of cover image and hidden message has been used to extract hidden message from stego image. In our method, transform domain steganography is formulated mathematically as a linear combination of sparse sources and therefore active steganalysis can be presented as a SCA problem. The feasibility of the SCA problem solving is confirmed by Linear Programming methods. Then, a fast algorithm is introduced to decrease the computational cost of steganalysis without much loss of accuracy. The accuracy of our new method has been confirmed in different experiments on a variety of transform domain steganography. These experiments show that, our method compared to the previous active steganalysis methods not only reduces the error rate but also decreases the computational cost. Manuscript profile
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  3 - A New and Robust Steganography Method for Data Hiding in JPEG Images
  A. R. Naghsh-Nilchi
  Data hiding is one of the effective solutions for the exchange of confidential messages in un-secure communication channels such as Internet. In this paper, we propose a new and effective steganography method for robust hiding of information in images with JPEG format. More

  Data hiding is one of the effective solutions for the exchange of confidential messages in un-secure communication channels such as Internet. In this paper, we propose a new and effective steganography method for robust hiding of information in images with JPEG format. The data is hidden in DCT coefficients of JPEG conversion of a bitmap image using a low quantization rate (for instance, 75% quantization rate) and then convert back to bitmap format. The new bitmap image is converted to JPEG format again, but with a higher quantization rate (90%, for instance). This resulted JPEG image, which carries the hidden data, is robust against the known attacks such as compatibility, F5, outguess and POV attacks. The experimental results show that although the available data capacity using the proposed algorithm is reduced and limited compare to the other similar methods, but it has a more effective robustness under the these attacks. Manuscript profile
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  4 - Stegananalysis Method Based on Co-Occurrence Matrix and Neural Network
  S. Ghanbari N. Ghanbari M. Keshtgari S. H. Nabavi Karizi
  Steganography is the art of hidden writing and secret communication. The goal of steganography is to hide the presence of information in other information. steganalysis is the art and science of detecting messages hidden using steganography. Co-occurrence matrix is the More

  Steganography is the art of hidden writing and secret communication. The goal of steganography is to hide the presence of information in other information. steganalysis is the art and science of detecting messages hidden using steganography. Co-occurrence matrix is the matrix containing information about the relationship between values of adjacent pixel in an image. In this paper, we extract features from Gray Level C0-occurrense Matrix (GLCM) that are difference between cover image (image without hidden information) and stego image (image with hidden information). In the proposed algorithm, first, we use a combined method of steganography based on both location and conversion to hide the information in the image. Then, using GLCM matrix properties, we investigate some difference values in the GLCM of the cover and stego images. We can extract features that were different between cover and stego images. Features are used for training neural network. This algorithm was tested on 800 standard image databases and it can detect 83% of stego images. Manuscript profile
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  5 - Blind Video Steganalysis by Semi-Supervised Approach for Motion Vectors Based Steganography Algorithms
  J.  Mortazavi Mehrizi M. Khademi H. Sadoghi Yazdi
  Supervised learning algorithms are widely used in blind video steganalysis and the cost of generating labeled data in them is high. That is why only a limited number of steganography algorithms with accessible code can be used for the training the classifier. Therefore, More

  Supervised learning algorithms are widely used in blind video steganalysis and the cost of generating labeled data in them is high. That is why only a limited number of steganography algorithms with accessible code can be used for the training the classifier. Therefore, we cannot be sure about the effectiveness of steganalyzer in identifying non-accessible video steganography algorithms. On the other hand, using offline classification methods in the blind video steganalysis causes the learning process be time consuming and the system cannot be updated online. To solve this problem, we propose a new method for the blind video steganalysis by semi-supervised learning approach. In the proposed method, by eliminating the limitation of labeled training dataset, the classifier performance is improved for video steganography algorithms with non-accessible code. It is also proved that the proposed method, compared to common classification methods for the blind video steganalysis, has less time complexity and it is an optimal online technique. The simulation results on the standard database show that in addition to the above advantages, this method has appropriate accuracy and is comparable to common methods. Manuscript profile
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  6 - Blind Video Steganalysis by Semi-Supervised Approach for Motion Vectors Based Steganography Algorithms
  J.  Mortazavi Mehrizi M. Khademi H. Sadoghi Yazdi
  Supervised learning algorithms are widely used in blind video steganalysis and the cost of generating labeled data in them is high. That is why only a limited number of steganography algorithms with accessible code can be used for the training the classifier. Therefore, More

  Supervised learning algorithms are widely used in blind video steganalysis and the cost of generating labeled data in them is high. That is why only a limited number of steganography algorithms with accessible code can be used for the training the classifier. Therefore, we cannot be sure about the effectiveness of steganalyzer in identifying non-accessible video steganography algorithms. On the other hand, using offline classification methods in the blind video steganalysis causes the learning process be time consuming and the system cannot be updated online. To solve this problem, we propose a new method for the blind video steganalysis by semi-supervised learning approach. In the proposed method, by eliminating the limitation of labeled training dataset, the classifier performance is improved for video steganography algorithms with non-accessible code. It is also proved that the proposed method, compared to common classification methods for the blind video steganalysis, has less time complexity and it is an optimal online technique. The simulation results on the standard database show that in addition to the above advantages, this method has appropriate accuracy and is comparable to common methods. Manuscript profile
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  7 - Reduce Dimensions of CDF Steganalysis Approach Using a Graph Theory Based Feature Selection Method
  S. Azadifar S. H. Khasteh M. H. Edrisi
  The steganalysis purpose is to prevent the pursuit of steganography methods for your goals. In steganography, in order to evaluate new ideas, there should be known steganalysis attacks on them, and the results should be compared with other existing methods. One of the m More

  The steganalysis purpose is to prevent the pursuit of steganography methods for your goals. In steganography, in order to evaluate new ideas, there should be known steganalysis attacks on them, and the results should be compared with other existing methods. One of the most well-known steganalysis methods is CDF method that used in this research. One of the major challenges in the image steganalysis issue is the large number of extracted features. High-dimensional data sets from two directions reduce steganalysis performance. On the one hand, with the increase in the dimensions of the data, the volume of computing increases, and on the other hand, a model based on high-dimensional data has a low generalization capability and increases probability of overfitting. As a result, reducing the dimensions of the problem can both reduce the computational complexity and improve the steganalysis performance. In this paper, has been tried to combine the concept of the maximum weighted clique problem and edge centrality measure, and to consider the suitability of each feature, to select the most effective features with minimum redundancy as the final features. The simulation results on the SPAM and CC-PEV data showed that the proposed method had a good performance and accurately obtained about 96% in the detection of data embedding in the images, and this method is more accurate than the previously known methods. Manuscript profile
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  8 - Improving Security of LSBM Steganography Using of Genetic Algorithm, Mmulti-Key and Blocking
  vajiheh sabeti Sepide faiazi hadise shirinkhah
  By increasing the precision of steganalysis attacks in discovering methods of steganography, the need to improve the security of steganographic methods is felt more than ever. The LSBM is one of the simplest methods of steganography, which have been proposed relatively More

  By increasing the precision of steganalysis attacks in discovering methods of steganography, the need to improve the security of steganographic methods is felt more than ever. The LSBM is one of the simplest methods of steganography, which have been proposed relatively successful attacks for its discovery. The main purpose of this paper is to provide a method for improving security of LSBM. The choice of the sequence of pixels to embed and how to modify them varies in LSBM-based methods. In most existing methods some of these decisions are made at random. In the proposed method in this paper, a multi-key idea in the first step and a genetic algorithm in the second step are used to make better decisions. In the proposed method, as MKGM, the image is blocked and GLSBM is executed for each block with different keys and finally the block with the least histogram change compared to the original block is included in the stego image. The GLSBM method is the same as the LSBM method except that the genetic algorithm is used to decide whether to increase or decrease non-matching pixels. Comparison of the image quality criteria and the accuracy of the attacks in the detection of the proposed method show that these criteria are improved compared to the original LSBM method. Manuscript profile
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  9 - Increasing Image Quality in Image Steganography Using Genetic Algorithm and Reversible Mapping
  Saeed TorabiTorbati مرتضی خادمی عباس ابراهیمی مقدم
  One of the evaluation methods for image steganography is preserving cover image quality and algorithm imperceptibility. Placing hidden information should be done in such a way that there is minimal change in quality between the cover image and the coded image (stego ima More

  One of the evaluation methods for image steganography is preserving cover image quality and algorithm imperceptibility. Placing hidden information should be done in such a way that there is minimal change in quality between the cover image and the coded image (stego image). The quality of the stego image is mainly influenced by the replacement method and the amount of hidden information or the replacement capacity. This can be treated as an optimization problem and a quality function can be considered for optimization. The variables of this function are the mappings applied to the cover image and the hidden information and location of the information. In the proposed method, by genetic algorithm and using the two concepts of targeted search and aimless search, the appropriate location and state for placement in the least significant bits of the cover image are identified. In this method, hidden information can be extracted completely and without error. This feature is important for management systems and cloud networks that use steganography to store information. Finally, the proposed method is tested and the results are compared with other methods in this field. The proposed method, in addition to maintaining the stego image quality, which is optimized based on PSNR, has also shown good performance in examining histogram and NIQE statistical criteria. Manuscript profile