Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Steganalysis: Using the blind deconvolution to retrieve the hidden data(2011) Jidesh, P.; George, S.Steganography has gained a substantial attention due to its application in wide areas. Steganography as it literally mean is hiding the information (stego data) inside the data (communication data) so that the receiver can only extract the desired information from the data. Steganalysis is the reverse process of steganography in which the information about the original data is hardly available, from the received data the extractor needs to identify the original data. Since this belong to a class of inverse problems it is hard to find the approximate match of the original data from the received one. In most of the cases this will fall under the category of ill-posed problems. The stego-data that has been embedded into the communication data can be considered as linear bounded operator operating on the input data and the reverse process (the Steganalysis) can be thought like a deconvolution problem by which we can extract the original data. Here we are assuming the watermarking as a linear operation with a bounded linear operator K : X→Y where X and Y are spaces of Bounded Variation (BV). The forward problem (the Steganography) is a direct convolution and the reverse (backward) problem (steganalysis) is a de-convolution procedure. In this work we are embedding a Gaussian random variable array with zero mean and with a specific variance into the data and we show how the original data can be extracted using the regularization method. The results are shown to substantiate the ability of the method to perform steganalysis. © 2011 IEEE.Item Shock coupled coherence enhancing diffusion for robust core-point detection in fingerprints(2011) Jidesh, P.; George, S.Enhancing the flow-like structures is important in forensic applications especially in fingerprint analysis. In most of the practical scenarios the poor quality of the off-line prints collected, adversely affect the verification process. Though there has been a plethora of methods proposed in literature for enhancing the degraded images, very few of them are suitable for enhancing the flow-like structures because they are ignorant of the coherence features present in images with dominant flow-like structures. In this paper we propose a method which enhances the fingerprint images with utmost consideration to the coherence features of the fingerprints. This method provides a shock at the inflection points while retaining the flow-like nature of the fingerprints. In other words, it enhances the coherence of features along with the edges. The experimental results shown endorses on the capability of the method to enhance the fingerprints which in turn will result in identification of the core-points in the fingerprints with a better accuracy. The core-point identification is a crucial step in fingerprint verification. © 2011 IEEE.Item Projection scheme for newton-type iterative method for Lavrentiev regularization(2012) Pareth, S.; George, S.In this paper we consider the finite dimensional realization of a Newton-type iterative method for obtaining an approximate solution to the nonlinear ill-posed operator equation F(x) = f, where F:D(F) ⊆ X → X is a nonlinear monotone operator defined on a real Hilbert space X. It is assumed that F(x̂) = f and that the only available data are f δ with ∥f - f δ∥ ≤ δ. It is proved that the proposed method has a local convergence of order three. The regularization parameter α is chosen according to the balancing principle considered by Perverzev and Schock (2005) and obtained an optimal order error bounds under a general source condition on x 0-x̂ (here x 0 is the initial approximation). The test example provided endorses the reliability and effectiveness of our method. © 2012 Springer-Verlag.Item Finite dimensional realization of a Guass-Newton method for ill-posed hammerstein type operator equations(2012) Erappa, M.E.; George, S.Finite dimensional realization of an iterative regularization method for approximately solving the non-linear ill-posed Hammerstein type operator equations KF(x) = f, is considered. The proposed method is a combination of the Tikhonov regularization and Guass-Newton method. The advantage of the proposed method is that, we use the Fr chet derivative of F only at one point in each iteration. We derive the error estimate under a general source condition and the regularization parameter is chosen according to balancing principle of Pereverzev and Schock (2005). The derived error estimate is of optimal order and the numerical example provided proves the efficiency of the proposed method. © 2012 Springer-Verlag.Item Ball Convergence of a Fifth-Order Method for Solving Equations Under Weak Conditions(Springer, 2021) Argyros, I.K.; George, S.; Erappa, S.M.We develop a ball convergence for a fifth-order method to find a solution for an equation. Earlier studies used conditions on the sixth derivative not present in the methods. Moreover, no error estimates are provided. That is why we used conditions up to the second derivative. Numerical experiments validate the theoretical results. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Ball Convergence of Multipoint Methods for Non-linear Systems(Springer Science and Business Media Deutschland GmbH, 2021) Argyros, I.K.; George, S.; Erappa, S.M.We study Multipoint methods using only the first derivative. Earlier studies use higher than three order derivatives not on the methods. Moreover Lipschitz constants are used to find error estimates not presented in earlier papers. Numerical examples complete this paper. © 2021, Springer Nature Singapore Pte Ltd.