A new watermarking method for vector map data: a study on information capacity of the carrier image

В данной статье приведено исследование метода встраивания ЦВЗ в векторные картографические данные на основе циклического сдвига списка вершин полигональных объектов. Предложена модификация метода, позволяющая обеспечить точную процедуру аутентификации, а также повысить стойкость метода к изменению содержимого карты. Основная идея улучшенного метода заключается в использовании шумоподобного изображения в качестве вторичного контейнера для ЦВЗ, представленного в форме битового вектора. Приведен алгоритм формирования изображения-контейнера на основе последовательности ЦВЗ, а также алгоритм извлечения такой последовательности. Проведено экспериментальное исследование информационной емкости изображения-контейнера и его стойкости к искажениям, моделирующим встраивание в реальные картографические данные: квантованию и добавлению шума. The paper presents a study of the watermarking method for vector map data based on the cyclic shift of the vertex list of polygonal objects. We propose a method modification to provide an accurate authentication procedure, as well as to enhance the method robustness against the map contents alteration. The main idea of the improved method is to use a noise-like image as a secondary container for a watermark represented in the form of a bit vector. An algorithm for the construction of an image container on the basis of the watermark sequence, as well as an algorithm for extraction such a sequence, are given. An experimental study on the information capacity of the carrier image and its robustness against distortions simulating the embedding into real cartographic data, such as quantization and noise addition, has been carried out.Исследование выполнено при финансовой поддержке РФФИ в рамках научных проектов № 19-07-00474 А и № 19-07-00138 А

A partition-based global optimization algorithm

Global optimization, Partition-based algorithm, DIRECT-type algorithm,

Towards Single- and Multiobjective Bayesian Global Optimization for Mixed Integer Problems

Bayesian Global Optimization (BGO) is a very efficient technique to optimize expensive evaluation problems. However, the application domain is limited to continuous search spaces when using a BGO algorithm. To solve mixed integer problems with a BGO algorithm, this paper adapts the heterogeneous distance function to construct the Kriging models and applies these new Kriging models in Multi-objective Bayesian Global Optimization (MOBGO). The proposed mixed integer MOBGO algorithm and the traditional MOBGO algorithm are compared on three mixed integer multi-objective optimization problems (MOP), w.r.t. the mean value of the hypervolume (HV) and the related standard deviation.</p

Towards Multi-objective Mixed Integer Evolution Strategies

Many problems are of a mixed integer nature, rather than being restricted to a single variable type. Although mixed integer algorithms exist for the single-objective case, work on the multi-objective case remains limited. Evolution strategies are stochastic optimisation algorithms that feature step size adaptation mechanisms and are typically used in continuous domains. More recently they were generalised to mixed integer problems. In this work, first steps are taken towards extending the single-objective mixed integer evolution strategy for the multi-objective case. First results are promising, but step size adaptation for the multi-objective case can likely be improved.</p

Survey of Piecewise Convex Maximization and PCMP over Spherical Sets

International audienceThe main investigation in this chapter is concerned with a piecewise convex function which can be defined by the pointwise minimum of convex functions, F(x)=min{f1(x),…,fm(x)}. Such piecewise convex functions closely approximate nonconvex functions, that seems to us as a natural extension of the piecewise affine approximation from convex analysis. Maximizing F(⋅ ) over a convex domain have been investigated during the last decade by carrying tools based mostly on linearization and affine separation. In this chapter, we present a brief overview of optimality conditions, methods, and some attempts to solve this difficult nonconvex optimization problem. We also review how the line search paradigm leads to a radius search paradigm, in the sense that sphere separation which seems to us more appropriate than the affine separation. Some simple, but illustrative, examples showing the issues in searching for a global solution are given