Markov chains with embeddings as steganography models are considered. Statistical estimators of the model parameters based on frequencies and correlation statistics are constructed and analysed. A polynomial algorithm for likelihood function computing is developed. The maximum likelihood estimators based on this algorithm are constructed. The results of computer experiments are presented.
Acta Technologica Agriculturae 1/2016Dušan Páleš et al.The most effective way for determination of curves for practical use is to use a set of control points. These control points can be accompanied by other restriction for the curve, for example boundary conditions or conditions for curve continuity (Sederberg, 2012). When a smooth curve runs only through some control points, we refer to curve approximation. The B-spline curve is one of such approximation curves and is addressed in this contribution. A special case of the B-spline curve is the Bézier curve Rédl et al., 2014). The B-spline curve is applied to a set of control points in a space, which were obtained by measurement of real vehicle movement on a slope (Rédl, 2007(Rédl, , 2008. Data were processed into the resulting trajectory (Rédl, 2012;Rédl and Kučera, 2008). Except for this, the movement of the vehicle was simulated using motion equations (Rédl, 2003;Rédl and Kročko, 2007). B-spline basis functionsBézier basis functions known as Bernstein polynomials are used in a formula as a weighting function for parametric representation of the curve (Shene, 2014). B-spline basis functions are applied similarly, although they are more complicated. They have two different properties in comparison with Bézier basis functions and these are: 1) solitary curve is divided by knots, 2) basis functions are not nonzero on the whole area. Every B-spline basis function is nonzero only on several neighbouring subintervals and thereby it is changed only locally, so the change of one control point influences only the near region around it and not the whole curve.These numbers are called knots, the set U is called the knot vector, and the half-opened interval 〈u i , u i + 1 ) is the i-th knot span. Seeing that knots u i may be equal, some knot spans may not exist, thus they are zero. If the knot u i appears p times, hence u i = u i + 1 = ... = u i + p -1 , where p >1, u i is a multiple knot of multiplicity p, written as u i (p). If u i is only a solitary knot, it is also called a simple knot. If the knots are equally spaced, i.e. (u i + 1 -u i ) = constant, for every 0 ≤ i ≤ (m -1), the knot vector or knot sequence is said uniform, otherwise it is non-uniform.Knots can be considered as division points that subdivide the interval 〈u 0 , u m 〉 into knot spans. All B-spline basis functions are supposed to have their domain on 〈u 0 , u m 〉. We will use u 0 = 0 and u m = 1.To define B-spline basis functions, we need one more parameter k, which gives the degree of these basis functions. Recursive formula is defined as follows:This definition is usually referred to as the Cox-de Boor recursion formula. If the degree is zero, i.e. k = 0, these basis functions are all step functions that follows from Eq. (1). N i, 0 (u) = 1 is only in the i-th knot span 〈u i , u i + 1 ). For example, if we have four knots u 0 = 0, u 1 = 1, u 2 = 2 and u 3 = 3, knot spans 0, 1 and 2 are 〈0, 1), 〈1, 2) and 〈2, 3), and the basis functions of degree 0 are N 0, 0 (u) = 1 on interval 〈0, 1) Acta In this co...
Рассматриваются возникающие в стеганографии задачи обнаружения вкраплений и статистического оценивания позиций, в которые вкраплены биты сообщения. В качестве моделей контейнера используются двоичные стационарные цепи Маркова как с известными, так и неизвестными матрицами переходных вероятностей. Построены статистические критерии, позволяющие выявить факт наличия вкраплений, основанные на статистиках серий и статистике отношения правдоподобия. Найдена асимптотическая мощность статистических критериев, основанных на статистиках серий, для семейства контигуальных альтернатив. Разработан алгоритм статистического оценивания позиций, в которые вкраплены биты сообщения, имеющий полиномиальную сложность. Представлены результаты компьютерных экспериментов.
О б р а з е ц ц и т и р о в а н и я:Волошко ВА, Вечерко ЕВ. Новые верхние границы для функции нецентрального хи-квадрат распределения. Журнал Белорусского государственного университета. Математика. Информатика. 2020;1:70-74 (на англ.). https://doi.org/10.33581/2520-6508-2020-1-70-74 F o r c i t a t i o n:Voloshko VA, Vecherko EV. New upper bounds for noncentral chi-square cdf. Journal of the Belarusian State University. Ma thematics and Informatics. 2020;1:70-74. https://doi.org/10.33581/2520-6508-2020-1-70-74 А в т о р ы: Валерий Анатольевич Волошко -кандидат физико-математических наук; старший научный сотрудник лаборатории математических методов защиты информации. Егор Валентинович Вечерко -кандидат физико-математических наук, доцент; заведующий лабораторией математических методов защиты информации. A u t h o r s:Valeriy A. Voloshko, PhD (physics and mathematics); senior researcher at the laboratory of mathematical methods of information security. valeravoloshko@yandex.ru https://orcid.org/0000000296930688 Egor V. Vecherko, PhD (physics and mathematics), docent; head of the laboratory of mathematical methods of information security. vecherko@bsu.by УДК 519.213 НОВЫЕ ВЕРХНИЕ ГРАНИЦЫ ДЛЯ ФУНКЦИИ НЕЦЕНТРАЛЬНОГО ХИ -КВАДРАТ РАСПРЕДЕЛЕНИЯ В. А. ВОЛОШКО 1) , Е. В. ВЕЧЕРКО 1) 1) Научноисследовательский институт прикладных проблем математики и информатики БГУ, пр. Независимости, 4, 220030, г. Минск, Беларусь Some new upper bounds for noncentral chi-square cumulative density function are derived from the basic symmetries of the multidimensional standard Gaussian distribution: unitary invariance, components independence in both polar and Cartesian coordinate systems. The proposed new bounds have analytically simple form compared to analogues available in the literature: they are based on combination of exponents, direct and inverse trigonometric functions, including hyperbolic ones, and the cdf of the one dimensional standard Gaussian law. These new bounds may be useful both in theory and in applications: for proving inequalities related to noncentral chi-square cumulative density function, and for bounding powers of Pearson's chi-squared tests.
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