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Universal image steganalysis using singular values of DCT coefficients
, Article 2013 10th International ISC Conference on Information Security and Cryptology ; 2013 ; Gaemmaghami, S ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
We propose a blind image steganalysis method based on the Singular Value Decomposition (SVD) of the Discrete Cosine Transform (DCT) coefficients that are revisited in this work. We compute geometric mean, mean of log values, and statistical moments (mean, variance and skewness) of the SVDs of the DCT sub-blocks that are averaged over the whole image to construct a 480-element feature vector for steganalysis. These features are fed to a Support Vector Machine (SVM) classifier to discriminate between stego and cover images. Experimental results show that the proposed method outperforms most powerful steganalyzers when applied to some well-known steganography algorithms
RISM: Single-Modal Image Registration via Rank-Induced Similarity Measure
, Article IEEE Transactions on Image Processing ; Volume 24, Issue 12 , 2015 , Pages 5567-5580 ; 10577149 (ISSN) ; Fatemizadeh, E ; Sharif University of Technology
Abstract
Similarity measure is an important block in image registration. Most traditional intensity-based similarity measures (e.g., sum-of-squared-difference, correlation coefficient, and mutual information) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation between pixel intensities; hence, perfect image registration cannot be achieved, especially in the presence of spatially varying intensity distortions. Here, we assume that spatially varying intensity distortion (such as bias field) is a low-rank matrix. Based on this assumption, we formulate the image registration problem as a nonlinear and low-rank matrix decomposition (NLLRMD)....
Adaptive singular value thresholding
, Article 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 442-445 ; 9781538615652 (ISBN) ; Marvasti, F ; Anbarjafari, G ; Kivinukk, A ; Tamberg, G ; Sharif University of Technology
Abstract
In this paper, we propose an Adaptive Singular Value Thresholding (ASVT) for low rank recovery under affine constraints. Unlike previous iterative methods that the threshold level is independent of the iteration number, in our proposed method, the threshold in adaptively decreases during iterations. The simulation results reveal that we get better performance with this thresholding strategy. © 2017 IEEE
Simulated annealing algorithm for absolute value equations
, Article International Journal of Operational Research ; Volume 30, Issue 1 , 2017 , Pages 142-150 ; 17457645 (ISSN) ; Jafari, H ; Ketabchi, S ; Sharif University of Technology
Abstract
The main goal of this paper is to compute the solution to the NP-hard absolute value equations (AVEs) Ax - |x| = b when the singular values of A exceed 1. First we show the AVE is equivalent to a bilinear programming problem and then we present a system tantamount to this problem. We use the simulated annealing (SA) algorithm to solve this system. Finally, several examples are given to illustrate the implementation and efficiency of the proposed method. Copyright © 2017 Inderscience Enterprises Ltd
Image steganalysis based on SVD and noise estimation: Improve sensitivity to spatial LSB embedding families
, Article IEEE Region 10 Annual International Conference, Proceedings/TENCON, 21 November 2011 through 24 November 2011, Bali ; 2011 , Pages 1266-1270 ; 9781457702556 (ISBN) ; Farhat, F ; Ghaemmaghami, S ; Sharif University of Technology
2011
Abstract
We propose a novel image steganalysis method, based on singular value decomposition and noise estimation, for the spatial domain LSB embedding families. We first define a content independence parameter, DS, that is calculated for each LSB embedding rate. Next, we estimate the DS curve and use noise estimation to improve the curve approximation accuracy. It is shown that the proposed approach gives an estimate of the LSB embedding rate, as well as information about the existence of the embedded message (if any). The proposed method can effectively be applied to a wide range of the image LSB steganography families in spatial domain. To evaluate the proposed scheme, we applied the method to a...
Universal image steganalysis against spatial-domain steganography based on energy distribution of singular values
, Article 7th International Conference on Information Technology and Application, ICITA 2011 ; 2011 , Pages 179-183 ; 9780980326741 (ISBN) ; Soltanian Zadeh, H ; Ghaemmagham, S ; Kamarei, M ; Sharif University of Technology
Abstract
A passive image steganalysis method is proposed to universally detect spatial-domain steganography schemes. It is shown to have better performance than universal steganalyzers known to be powerful in spatial domain, including the WFLogSv and the WAM methods. This level of accuracy is the result of improving the WFLogSv steganalyzer by considering a more comprehensive relationship between the singular values of each image block and the linear correlation of the rows and the columns. That is, instead of the closeness of the lower singular values to zero, the energy distribution of the singular values is investigated. An innovative measure is proposed for this investigation, which is inspired...
Some lower bounds for the energy of graphs
, Article Linear Algebra and Its Applications ; Volume 591 , 2020 , Pages 205-214 ; Ghodrati, A. H ; Hosseinzadeh, M. A ; Sharif University of Technology
Elsevier Inc
2020
Abstract
The singular values of a matrix A are defined as the square roots of the eigenvalues of A⁎A, and the energy of A denoted by E(A) is the sum of its singular values. The energy of a graph G, E(G), is defined as the sum of absolute values of the eigenvalues of its adjacency matrix. In this paper, we prove that if A is a Hermitian matrix with the block form A=(BDD⁎C), then E(A)≥2E(D). Also, we show that if G is a graph and H is a spanning subgraph of G such that E(H) is an edge cut of G, then E(H)≤E(G), i.e., adding any number of edges to each part of a bipartite graph does not decrease its energy. Let G be a connected graph of order n and size m with the adjacency matrix A. It is well-known...
Image adaptive semi-fragile watermarking scheme based on RDWT-SVD
, Article 2008 International Conference on Innovations in Information Technology, IIT 2008, Al Ain, 16 December 2008 through 18 December 2008 ; February , 2008 , Pages 130-134 ; 9781424433971 (ISBN) ; Ghaemmaghami, S ; Sharif University of Technology
2008
Abstract
One of the main properties of semi-fragile image watermarking methods is robustness against lossy compressions, e.g. JPEG conversion. In this paper, robustness of a semi-fragile watermarking scheme based on DWT (Discrete Wavelet Transform), coupled with SVD (Singular Value Decomposition), is improved. To achieve this goal a traditional, critically sampled wavelet transform is replaced by a redundant wavelet transform. The proposed approach is compared to the DWT-SVD based watermarking, introduced earlier, and significantly greater robustness under the JPEG compression is presented. ©2008 IEEE
Hierarchical co-clustering for web queries and selected URLs
, Article 8th International Conference on Web Information Systems Engineering, WISE 2007, Nancy, 3 December 2007 through 7 December 2007 ; Volume 4831 LNCS , 2007 , Pages 653-662 ; 03029743 (ISSN); 9783540769927 (ISBN) ; Abolhassani, H ; Sharif University of Technology
Springer Verlag
2007
Abstract
Recently query log mining is extensively used by web information systems. In this paper a new hierarchical co-clustering for queries and URLs of a search engine log is introduced. In this method, firstly we construct a bipartite graph for queries and visited URLs, and then to discover noiseless clusters, all queries and related URLs are projected in a reduced dimensional space by applying singular value decomposition. Finally, all queries and URLs are iteratively clustered for constructing hierarchical categorization. The method has been evaluated using a real world data set and shows promising results. © Springer-Verlag Berlin Heidelberg 2007
Application of Error Potential in Brain-Computer Interface Systems
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Brain computer interfaces (BCI) are systems designed to understand the brain function from activation patterns and dynamics recorded from the brain activity and use this knowledge to give disabled people the ability to communicate with their surroundings. Features are extracted from recorded signals from the brain while occupied in a mental task and classified into categories related to the task given. These classifiers are then used for the estimation of user anticipation. Usually, the tasks defined are meant to evoke or induce a potential in the pattern of the brain. Awareness of error responses is one of the cognitive functions of the brain which occurs when a response is in conflict with...
Analysis of Sensitivity of Features to Data Embedding in Blind Image Steganalysis
, M.Sc. Thesis Sharif University of Technology ; Ghaemmaghami, Shahrokh (Supervisor)
Abstract
Steganalysis is the science of detecting covert communication. It is called blind (universal) if designed to detect stego images steganographied by a wide range of embedding methods. In this method, statistical properties of the image are explored, regardless the embedding procedure employed. The main problem for image steganalysis is to find sensitive features and characteristics of the image which make a statistically significant difference between the clean and stego images. In this thesis we propose a blind image steganalysis method based on the singular value decomposition (SVD) of the discrete cosine transform (DCT) coefficients that are revisited in this work in order to enhance the...
Some Applications of Singular Values Decomposition in Image Processing
, M.Sc. Thesis Sharif University of Technology ; Mahdavi Amiri, Nezamoddin (Supervisor)
Abstract
The field of digital image processing refers to processing digital images by means of a digital computer. One of the principal applications used in digital image processing is image compression. Image compression is the problem of reducing the amount of data required to represent a digital image. The basis of the reduction process is the removal of redundant data. One of the other principal applications of image processing is noise reduction (filtering) of images corrupted with additive noise. Filtering techniques are oriented toward modeling the degradation and applying the inverse process in order to recover the original image. Image watermarking is another application of image processing....
Decoding the long term memory using weighted thresholding union subspaces based classification on magnetoencephalogram
, Article Communications in Computer and Information Science ; Vol. 427, issue , 2014 , p. 164-171 ; ISSN: 18650929 ; ISBN: 9783319108483 ; Fatemizadeh, E ; Sharif University of Technology
Abstract
In this paper Long Term Memory (LTM) process during leftward and rightward orientation recalling have been analyzed using Magnetoencephalogram (MEG) signals. This paper presents a novel criterion for decision making using union subspace based classifier. The proposed method involves the Eigenvalues from Singular Value Decomposition (SVD) of each subspace not only to select basis for each subspace but also to weight the decision making criterion to discriminate two classes. The proposed method has provided orientation detection from recalling signal with 6.75 percent increase in classification accuracy compared to better results on this data
Learning overcomplete dictionaries based on parallel atom-updating
, Article IEEE International Workshop on Machine Learning for Signal Processing, MLSP ; 2013 ; 21610363 (ISSN) ; 9781479911806 (ISBN) ; Babaie-Zadeh, M ; Jutten, C ; IEEE Signal Processing Society ; Sharif University of Technology
2013
Abstract
In this paper we propose a fast and efficient algorithm for learning overcomplete dictionaries. The proposed algorithm is indeed an alternative to the well-known K-Singular Value Decomposition (K-SVD) algorithm. The main drawback of K-SVD is its high computational load especially in high-dimensional problems. This is due to the fact that in the dictionary update stage of this algorithm an SVD is performed to update each column of the dictionary. Our proposed algorithm avoids performing SVD and instead uses a special form of alternating minimization. In this way, as our simulations on both synthetic and real data show, our algorithm outperforms K-SVD in both computational load and the quality...
SVD analysis of dynamic properties for fatigue loaded intervertebral disc
, Article 2011 1st Middle East Conference on Biomedical Engineering, MECBME 2011, 21 February 2011 through 24 February 2011, Sharjah ; 2011 , Pages 32-36 ; 9781424470006 (ISBN) ; Malik, A. S ; Wang, J. L ; Parnianpour, M ; Sharif University of Technology
2011
Abstract
This paper uses singular value decomposition (SVD) for studying the dynamic properties of fatigue-loaded intervertebral disc. Previously, this problem had been addressed using mathematical models of using mass, spring and damper or based on poroelastic theory. This paper utilizes the signal processing approach and attempts to describe SVD based feature that can be an indicator for change in behavioral performance of the intervertebral disc warning the occurrence of temporary or permanent change in the structure or abnormality in behavior. The results are encouraging; however, further validation is required with more data
Towards higher detection accuracy in blind steganalysis of JPEG images
, Article 24th Iranian Conference on Electrical Engineering, ICEE 2016, 10 May 2016 through 12 May 2016 ; 2016 , Pages 1860-1864 ; 9781467387897 (ISBN) ; Heidari, M ; Ghaemmaghami, S ; Gholampour, I ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
A new steganalysis system for JPG-based image data hiding is proposed in this paper. We use features extracted from both wavelet and DCT domains that are refined later in the sense of utmost discrimination between the clear and stego images in the classification system. Statistical properties of the SVD of wavelet sub-bands are combined with the extended DCT-Markov features, and the features that are most sensitive to the data embedding are chosen through a SVM-RFE based selection algorithm. Experimental results show significant improvement over baseline methods, especially for steganalysis of Perturbed Quantization (PQ), which is known to be one of most secure JPG-based steganography...
Edge addition, singular values, and energy of graphs and matrices
, Article Linear Algebra and Its Applications ; Volume 430, Issue 8-9 , 2009 , Pages 2192-2199 ; 00243795 (ISSN) ; Ghorbani, E ; Oboudi, M. R ; Sharif University of Technology
2009
Abstract
The energy of a graph/matrix is the sum of the absolute values of its eigenvalues. We investigate the result of duplicating/removing an edge to the energy of a graph. We also deal with the problem that which graphs G have the property that if the edges of G are covered by some subgraphs, then the energy of G does not exceed the sum of the subgraphs' energies. The problems are addressed in the general setting of energy of matrices which leads us to consider the singular values too. Among the other results it is shown that the energy of a complete multipartite graph increases if a new edge added or an old edge is deleted. © 2008 Elsevier Inc. All rights reserved
Median filtering forensics in compressed video
, Article IEEE Signal Processing Letters ; Volume 26, Issue 2 , 2019 , Pages 287-291 ; 10709908 (ISSN) ; Ghaemmaghami, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Median filtering has received extensive attention from forensics analyzers, as a common content-preserving, smoothing, and denoising manipulation. We propose a detection scheme for median filtering of video sequences in compressed domain based on the singular value decomposition of the process matrix, which approximates the median filtering. Projection over some of the eigenspaces of the process matrix gives a set of features of small dimension, even as small as three, making the proposed scheme a fast and suitable detector for video median filtering. The experimental evaluations show that the proposed method outperforms the state-of-the-art detectors of median filtering, and its edge...
Face recognition using the combination of weighted sparse representation-based classification and singular value decomposition face
, Article Indian Journal of Pharmaceutical Sciences ; Volume 82 , 2020 , Pages 91-97 ; Vahidi, J ; Ghaffari, A ; Motameni, H ; Sharif University of Technology
Indian Pharmaceutical Association
2020
Abstract
Given the increasing need for the creation and development of automated systems, the problem of detecting and identifying the faces of people in the images has been considered by the researchers. In recent years, the sparse representation based classification has been of great interest to researchers. The goal of this investigation is to provide a quick and effective way to identify faces based on the sparse representation. Since the basis of sparse representation is to calculate it through L1-norm optimization for high dimensional dictionary with high computational volume, a smoothed L0-norm optimization-based method was introduced. At the time of obtaining the sparse representation using...
A method for eye detection based on SVD transforms
, Article International Journal of Imaging Systems and Technology ; Volume 16, Issue 5 , 2006 , Pages 222-229 ; 08999457 (ISSN) ; Taghavi Sheikh, L ; Tavakoli Targhi, A. R ; Sharif University of Technology
2006
Abstract
A set of transforms (SVD transforms) were introduced in (Shahshahani and Tavakoli Targhi) for understanding images. These transforms have been applied to some problems in computer vision including segmentation, detection of objects in a texture environment, classification of textures, detection of cracks or other imperfections, etc. This technique is shown to be applicable to determination of the location of eyes in a facial image. This method makes no use of color cues, prior geometric knowledge or other assumptions and does not require training. It is also insensitive to local perturbations in lighting, change of orientation and pose, scaling, and complexity of the background including...