## Summary

### BibTeX

@MISC{Baragada_summary,

author = {Sambasiva Rao Baragada and S. Ramakrishna and M. S. Rao and S. Purushothaman},

title = {Summary},

year = {}

}

### OpenURL

### Abstract

Steganalysis plays an important role in identifying unacceptable information transmitted through internet communication system. In the process of steganalysis many untoward incidents can be avoided. Many techniques have been proposed and new techniques are tried with different combinations to maximize the efficiency of retrieving hidden information. We have proposed a combination of polynomial vector with Fisher’s discriminant function using the information of bitplane and radial basis neural network (PVDRBF). Each set of pixel is preprocessed to obtain interpolated pixels using PDV. This is further trained by Fisher’s discriminant method that transforms once again into 2-dimensional vector. A processing of training the RBF is adopted to obtain set of final weights. During implementation, the final weights are used to classify the presence of hidden information. Key words:

### Citations

966 |
The use of multiple measurements in taxonomic problems
- Fisher
- 1936
(Show Context)
Citation Context ...erns of higher dimensions (more than two dimensions) can be seen on a two dimensional graph. The mapping operation can be linear or non-linear. R.A. Fisher developed a linear classification algorithm =-=[18]-=- and a method for constructing a classifier on the optimal discriminant plane, with minimum distance criterion for multi-class classification with small number of patterns [19]. The method of consider... |

302 | On The Limits of Steganography
- Anderson, Petitcolas
- 1998
(Show Context)
Citation Context ...or, Interpolation, bitplane, Steganalysis, Carrier image, Covert image. 1. Introduction Steganalysis is the process of identifying the presence of hidden information in a text, image, audio, or video =-=[1,2,3]-=-. Most of the present literature on steganalysis follows either a parametric model [10, 11, 12] or a blind model [4, 5, 6, 7, 8, 9]. A generic steganalysis method that can attack steganography blindly... |

245 |
Tukey, “A projection pursuit algorithm for exploratory data analysis
- Friedman, W
- 1974
(Show Context)
Citation Context ...l space. Some of the linear mapping algorithms are principal component mapping [22], generalized declustering mapping [23, 24, 25, 26], least squared error mapping [27] and projection pursuit mapping =-=[28]-=-. In this work, the generalized declustering optimal discriminant plane is used. The mapping of the original pattern ‘X’ onto a new vector ‘Y’ on a plane is done by a matrix transformation, which is g... |

190 |
The prisoner’s problem and the subliminal channel
- Simmons
- 1984
(Show Context)
Citation Context ...or, Interpolation, bitplane, Steganalysis, Carrier image, Covert image. 1. Introduction Steganalysis is the process of identifying the presence of hidden information in a text, image, audio, or video =-=[1,2,3]-=-. Most of the present literature on steganalysis follows either a parametric model [10, 11, 12] or a blind model [4, 5, 6, 7, 8, 9]. A generic steganalysis method that can attack steganography blindly... |

181 | Exploring Steganography: Seeing the Unseen
- Johnson, Jajodia
- 1998
(Show Context)
Citation Context ...or, Interpolation, bitplane, Steganalysis, Carrier image, Covert image. 1. Introduction Steganalysis is the process of identifying the presence of hidden information in a text, image, audio, or video =-=[1,2,3]-=-. Most of the present literature on steganalysis follows either a parametric model [10, 11, 12] or a blind model [4, 5, 6, 7, 8, 9]. A generic steganalysis method that can attack steganography blindly... |

85 | Detecting hidden messages using higher-order statistical models
- Farid
- 2002
(Show Context)
Citation Context ...ganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applications. A framework for steganalysis based on supervised learning has been designed in =-=[13]-=-. The framework was further developed and tested. A mere significant work has been carried out on supervised steganalysis, using neural networks as a classifier [14, 15]. Fishers’ linear discriminant ... |

74 |
The Singular Value Decomposition: Its Computation and Some Applications
- Klema, Laub
- 1980
(Show Context)
Citation Context ...ven for a more general discriminating analysis and multi-orthonormal vectors [31, 32, 33]. If the determinant of SW is zero, then singular value decomposition (SVD) on SW has to be done. On using SVD =-=[34, 35]-=-, SW is decomposed into three matrices U, W and V. The matrices U and W are unitary matrices, and V is a diagonal matrix with non-negative diagonal elements arranged in the decreasing order. A small v... |

64 | Detecting steganographic content on the internet
- Provos, Honeyman
(Show Context)
Citation Context ...dentifying the presence of hidden information in a text, image, audio, or video [1,2,3]. Most of the present literature on steganalysis follows either a parametric model [10, 11, 12] or a blind model =-=[4, 5, 6, 7, 8, 9]-=-. A generic steganalysis method that can attack steganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applications. A framework for steganalysis... |

55 |
An optimal set of discriminant vectors
- Foley, Sammon
- 1975
(Show Context)
Citation Context ... is the discriminant vector ϕ2. In Eq.(12) and Eq. (14), SW should be non-singular. The SW matrix should be non-singular, even for a more general discriminating analysis and multi-orthonormal vectors =-=[31, 32, 33]-=-. If the determinant of SW is zero, then singular value decomposition (SVD) on SW has to be done. On using SVD [34, 35], SW is decomposed into three matrices U, W and V. The matrices U and W are unita... |

52 | Steganalysis of additive noise modelable information hiding
- Harmsen, Pearlman
- 2003
(Show Context)
Citation Context ...dentifying the presence of hidden information in a text, image, audio, or video [1,2,3]. Most of the present literature on steganalysis follows either a parametric model [10, 11, 12] or a blind model =-=[4, 5, 6, 7, 8, 9]-=-. A generic steganalysis method that can attack steganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applications. A framework for steganalysis... |

39 | Steganalysis using color wavelet statistics and one-class support vector machines
- Lyu, Farid
(Show Context)
Citation Context ...eganalysis is the process of identifying the presence of hidden information in a text, image, audio, or video [1,2,3]. Most of the present literature on steganalysis follows either a parametric model =-=[10, 11, 12]-=- or a blind model [4, 5, 6, 7, 8, 9]. A generic steganalysis method that can attack steganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applic... |

33 |
An overview of mapping techniques for exploratory pattern analysis
- Siedlecki, Siedlecka, et al.
- 1988
(Show Context)
Citation Context ...plane is done by a matrix transformation, which is given by Where Y=AX (7) (8) and ϕ1 and ϕ2 are the discriminant vectors (also called projection vectors). An overview of different mapping techniques =-=[29, 30]-=- is addressed earlier. The vectors ϕ1 and ϕ2 are obtained 2.3.1) Computation of discriminant vectors ϕ1 and ϕ2 The criterion to evaluate the classification performance is given by: (9) Where Sb the be... |

31 |
Lebesgue functions for polynomial interpolation - a survey
- Brutman
(Show Context)
Citation Context ...pattern set by a polynomial set obtained by outer producting the given pattern. It can also be described as, given some points, the aim is to find a polynomial which goes exactly through these points =-=[16, 17]-=-. Polynomial v) Linear plus NL2 (NL5) The pre-processed input vector is a 6-dimensional vector. (5) vi) Linear plus NL3 (NL6) (6) The pre-processed input vector is a 9-dimensional vector. In Eq. (1) t... |

30 |
F Fogelman-Soulie F., “On The Relations Between Discriminant Analysis and Multilayer Perceptrons
- Gallinari, Badran
- 1991
(Show Context)
Citation Context ...s classification with small number of patterns [19]. The method of considering the number of patterns and feature size [21], and the relations between discriminant analysis and multilayer perceptrons =-=[20]-=- has been addressed earlier. A linear mapping is used to map an n-dimensional vector space ℜ n onto a two dimensional space. Some of the linear mapping algorithms are principal component mapping [22],... |

29 |
Optimal Discriminant Plane for a Small Number of
- Hong, Yang
- 1991
(Show Context)
Citation Context ...lassification algorithm [18] and a method for constructing a classifier on the optimal discriminant plane, with minimum distance criterion for multi-class classification with small number of patterns =-=[19]-=-. The method of considering the number of patterns and feature size [21], and the relations between discriminant analysis and multilayer perceptrons [20] has been addressed earlier. A linear mapping i... |

27 |
Steganalysis of lsb encoding in color images
- Fridrich, Du, et al.
- 2000
(Show Context)
Citation Context ...eganalysis is the process of identifying the presence of hidden information in a text, image, audio, or video [1,2,3]. Most of the present literature on steganalysis follows either a parametric model =-=[10, 11, 12]-=- or a blind model [4, 5, 6, 7, 8, 9]. A generic steganalysis method that can attack steganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applic... |

24 |
Considerations of sample and feature size
- Foley
- 1972
(Show Context)
Citation Context ...on the optimal discriminant plane, with minimum distance criterion for multi-class classification with small number of patterns [19]. The method of considering the number of patterns and feature size =-=[21]-=-, and the relations between discriminant analysis and multilayer perceptrons [20] has been addressed earlier. A linear mapping is used to map an n-dimensional vector space ℜ n onto a two dimensional s... |

17 | A mathematical framework for active steganalysis, Special issue on multimedia watermarking
- Chandramouli
(Show Context)
Citation Context ...eganalysis is the process of identifying the presence of hidden information in a text, image, audio, or video [1,2,3]. Most of the present literature on steganalysis follows either a parametric model =-=[10, 11, 12]-=- or a blind model [4, 5, 6, 7, 8, 9]. A generic steganalysis method that can attack steganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applic... |

17 |
J.-Y.: Optimal Fisher discriminant analysis using the rank decomposition
- Cheng, Zhuang, et al.
- 1992
(Show Context)
Citation Context ... is the discriminant vector ϕ2. In Eq.(12) and Eq. (14), SW should be non-singular. The SW matrix should be non-singular, even for a more general discriminating analysis and multi-orthonormal vectors =-=[31, 32, 33]-=-. If the determinant of SW is zero, then singular value decomposition (SVD) on SW has to be done. On using SVD [34, 35], SW is decomposed into three matrices U, W and V. The matrices U and W are unita... |

14 | Secret Key Estimation in Sequential Steganography
- Trivedi, Chandramouli
- 2005
(Show Context)
Citation Context ...dentifying the presence of hidden information in a text, image, audio, or video [1,2,3]. Most of the present literature on steganalysis follows either a parametric model [10, 11, 12] or a blind model =-=[4, 5, 6, 7, 8, 9]-=-. A generic steganalysis method that can attack steganography blindly, detect hidden data without knowing embedding methods, will be more useful in practical applications. A framework for steganalysis... |

12 | Steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network
- Shi, Xuan, et al.
- 2005
(Show Context)
Citation Context ...ised learning has been designed in [13]. The framework was further developed and tested. A mere significant work has been carried out on supervised steganalysis, using neural networks as a classifier =-=[14, 15]-=-. Fishers’ linear discriminant function (FLD) as a classifier has shown impressive results for steganalysis work [37]. We extend the present neural network based steganalytic work by combining a polyn... |

9 |
Interactive Pattern Analysis and Classification
- Sammon
- 1970
(Show Context)
Citation Context ...ear mapping is used to map an n-dimensional vector space ℜ n onto a two dimensional space. Some of the linear mapping algorithms are principal component mapping [22], generalized declustering mapping =-=[23, 24, 25, 26]-=-, least squared error mapping [27] and projection pursuit mapping [28]. In this work, the generalized declustering optimal discriminant plane is used. The mapping of the original pattern ‘X’ onto a ne... |

9 |
On the Use of Singular Value Decomposition and Decimation in Discrete-Time Band-Limited Signal Extrapolation
- Sullivan
- 1984
(Show Context)
Citation Context ...ven for a more general discriminating analysis and multi-orthonormal vectors [31, 32, 33]. If the determinant of SW is zero, then singular value decomposition (SVD) on SW has to be done. On using SVD =-=[34, 35]-=-, SW is decomposed into three matrices U, W and V. The matrices U and W are unitary matrices, and V is a diagonal matrix with non-negative diagonal elements arranged in the decreasing order. A small v... |

8 |
An optimal discriminant plane
- Sammon
- 1970
(Show Context)
Citation Context ...ear mapping is used to map an n-dimensional vector space ℜ n onto a two dimensional space. Some of the linear mapping algorithms are principal component mapping [22], generalized declustering mapping =-=[23, 24, 25, 26]-=-, least squared error mapping [27] and projection pursuit mapping [28]. In this work, the generalized declustering optimal discriminant plane is used. The mapping of the original pattern ‘X’ onto a ne... |

4 |
Attacks on steganographic systems,” Third Information Hiding Workshop
- Westfeld, Pfitzmann
- 1999
(Show Context)
Citation Context |

4 |
Implementation of Radial Basis Function Neural Network for Image Steganalysis
- Baragada, Rao, et al.
(Show Context)
Citation Context ... carried out on supervised steganalysis, using neural networks as a classifier [14, 15]. Fishers’ linear discriminant function (FLD) as a classifier has shown impressive results for steganalysis work =-=[37]-=-. We extend the present neural network based steganalytic work by combining a polynomial interpolated fisher’s discriminant method with the radial basis function neural network function. 2. Methodolog... |

3 |
Ning Dai, Jian-Jing Shen, “Feature Selection Based on Bhattacharyya Distance: A Generalized Rough Set Method
- Sun, Han
- 2006
(Show Context)
Citation Context |

3 |
Soft Computing Approach to Steganalysis of LSB Embedding
- Benton, Chu
- 2005
(Show Context)
Citation Context ...ised learning has been designed in [13]. The framework was further developed and tested. A mere significant work has been carried out on supervised steganalysis, using neural networks as a classifier =-=[14, 15]-=-. Fishers’ linear discriminant function (FLD) as a classifier has shown impressive results for steganalysis work [37]. We extend the present neural network based steganalytic work by combining a polyn... |

3 |
P.C.Young P.C, “Approach to Feature Selection Based on the Karhunen-Loeve Expansion
- Kittler
- 1973
(Show Context)
Citation Context ... [20] has been addressed earlier. A linear mapping is used to map an n-dimensional vector space ℜ n onto a two dimensional space. Some of the linear mapping algorithms are principal component mapping =-=[22]-=-, generalized declustering mapping [23, 24, 25, 26], least squared error mapping [27] and projection pursuit mapping [28]. In this work, the generalized declustering optimal discriminant plane is used... |

3 |
A Declustering Criterion for Feature Extraction in Pattern Recognition
- Fehlauer, Eisenstein
- 1978
(Show Context)
Citation Context ...ear mapping is used to map an n-dimensional vector space ℜ n onto a two dimensional space. Some of the linear mapping algorithms are principal component mapping [22], generalized declustering mapping =-=[23, 24, 25, 26]-=-, least squared error mapping [27] and projection pursuit mapping [28]. In this work, the generalized declustering optimal discriminant plane is used. The mapping of the original pattern ‘X’ onto a ne... |

3 |
Mapping Algorithms in ISPAHAN,” pattern Recognition
- Gelsema
- 1980
(Show Context)
Citation Context |

3 |
Dimensionality Reduction Techniques Based on a Least Squared Error Criterion
- Mix, “A
- 1982
(Show Context)
Citation Context ...ctor space ℜ n onto a two dimensional space. Some of the linear mapping algorithms are principal component mapping [22], generalized declustering mapping [23, 24, 25, 26], least squared error mapping =-=[27]-=- and projection pursuit mapping [28]. In this work, the generalized declustering optimal discriminant plane is used. The mapping of the original pattern ‘X’ onto a new vector ‘Y’ on a plane is done by... |

3 |
J.Y.Yang J.Y., “A Generalized Optimal Set of Discriminant Vectors
- Liu
- 1992
(Show Context)
Citation Context ... is the discriminant vector ϕ2. In Eq.(12) and Eq. (14), SW should be non-singular. The SW matrix should be non-singular, even for a more general discriminating analysis and multi-orthonormal vectors =-=[31, 32, 33]-=-. If the determinant of SW is zero, then singular value decomposition (SVD) on SW has to be done. On using SVD [34, 35], SW is decomposed into three matrices U, W and V. The matrices U and W are unita... |

3 |
Er,.Shiqian Wu, Juwei Lu and Hock Lye Toh, “Face Recognition with Radial BasisFunction(RBF) Neural Networks
- Joo
- 2002
(Show Context)
Citation Context ...es (or increases) monotonically with distance from a central point. The centre, the distance scale, and the precise shape of the radial function are parameters of the model, all fixed if it is linear =-=[36]-=-. A typical radial function is the Gaussian which, in the case of a scalar input, is h(x)=exp((-(x-c) 2 )/(r 2 )) (18) Its parameters are its centre c and its radius r. RBF networks have traditionally... |

2 |
Lingdi Ping, “Effective steganalysis based on statistical moments of differential characteristic function
- Liu, Pan, et al.
- 2006
(Show Context)
Citation Context |

1 |
An Introduction to Numerical Analysis (2nd ed.), Chapter 3
- Atkinson
- 1988
(Show Context)
Citation Context ...pattern set by a polynomial set obtained by outer producting the given pattern. It can also be described as, given some points, the aim is to find a polynomial which goes exactly through these points =-=[16, 17]-=-. Polynomial v) Linear plus NL2 (NL5) The pre-processed input vector is a 6-dimensional vector. (5) vi) Linear plus NL3 (NL6) (6) The pre-processed input vector is a 9-dimensional vector. In Eq. (1) t... |

1 |
Polynomial Discriminant Radial Basis Function for Steganalysis
- Baragada, Rao, et al.
- 2009
(Show Context)
Citation Context ...nificant work has been carried out on supervised steganalysis, using neural networks as a classifier [14, 15, 26-32]. Polynomial processed vector has shown impressive results for steganalysis work in =-=[18]-=-. We tried to present another combination of polynomial vector discriminant with back propagation algorithm neural network. (a) (b) Fig. 1 a) Flowchart for training, b) flowchart for testing Manuscrip... |

1 | Kurugollu F,”SVD Based Universal Spatial Domain Image Steganalysis - Gul - 2010 |

1 | Kuijun Lu; Xianting Zeng; Xuezeng Pan, “Feature-Based Steganalysis for - Li - 2009 |

1 | Aiming Wang, “An Investigation of Genetic Algorithm on - Yu - 2009 |

1 | Shang-Ping Zhong, “Blind steganalysis method for BMP images based on statistical - Zhang - 2009 |

1 | Aiming Wang, “Steganalysis Based on Bayesion Network and - Yu - 2009 |

1 | Xiangyang Luo, ”A wavelet-based blind JPEG image steganalysis using co-occurrence matrix - Zong, Liu - 2009 |

1 | Xiangyang Luo, Chunfang Yang, “Classification between - He, Liu - 2009 |

1 | Zhiping Zhou, “An Image Steganalysis Method Based on Characteristic Function - Sun, Li, et al. - 2009 |

1 | Reduced complexity enhancement of steganalysis of LSB-matching image steganography - Malekmohamadi, Ghaemmaghami - 2009 |

1 | Steganography detection using - Agaian, Chen, et al. - 2008 |

1 |
Guodong Wang, “A blind image steganalysis based on features from three domains
- Liu, Huang, et al.
- 2008
(Show Context)
Citation Context ...has been noticed that the present literature on steganalysis is broadly categorized as supervised learning model, parametric model [10, 11, 12], blind model [4, 5, 6, 7, 8, 9, 19-25] and hybrid model =-=[29]-=-. A generic steganalysis method that can attack steganography Sun college of Engineering and Technology Nagerkoil, INDIA blindly, detect hidden data without knowing embedding methods, will be more use... |

1 | A novel steganalysis of LSB matching based on kernel FDA - Hu, Jiang, et al. - 2008 |

1 | Qingzhong Liu Sung, A.H, “Building resilient classifiers for LSB matching steganography - Ferreira, Silva, et al. - 2008 |