Математичні моделі змагальних атак на системи розпізнавання образів

Робота складається з 3 розділів, містить 47 ілюстрацій, 1 таблиця, 32 літературних посилання, обсяг роботи - 102 сторінки. Завданням роботи є огляд різних змагальних атак на системи розпізнавання образів, вибір алгоритмів нейронних мереж для класифікації зображень, їх детальний опис та програмна реалізація на вибраних базах даних. Мета цієї дипломної роботи полягає у досліджені і реалізації змагальних атак на системи розпізнавання образів та огляд отриманих результатів. Об’єктом дослідження є процес реалізації змагальних атак на моделі розпізнавання образів. Предметом дослідження є алгоритми змагальних атак та моделей розпізнавання. Актуальність роботи зумовлюється тим, що на сьогоднішній день питання якісного розпізнавання образів є актуальним, як і побудова захисту таких систем. Методами дослідження дипломної роботи складають методи системного, порівняльного і статистичного аналізу, логіко-діалектичний метод пізнання, синтетичних та експертних оцінок, метод логічного узагальнення та синтезу. Вони базуються на використанні методів статистичного якісного і кількісного порівняння, наукової абстракції, факторного та структурного аналізу. Використано широке коло зарубіжних та вітчизняних літературних та електронних джерел. Наукова новизна одержаних результатів дослідження полягає в тому, що на підставі проведеного теоретико - методологічного аналізу побудовано модель змагальних атак на системи розпізнавання образів та показано їх вразливості, що можна використати для покращення систем захисту. Практичне застосування полягає в тому, що результати роботи можуть бути використані для побудови захищених моделей розпізнавання образів в різних установах.The work consists of 3 sections, contains 47 illustrations, 1 table, 32 literary references, the volume of the work is 102 pages. The task of the work is an overview of various adversarial attacks on pattern recognition systems, a selection of neural network algorithms for image classification, their detailed description and software implementation on selected databases. The purpose of this thesis is to research and implement adversarial attacks on pattern recognition systems and review the results. The object of research is the process of implementing adversarial attacks on pattern recognition models. The subject of research is the algorithms of adversarial attacks and recognition models. The relevance of the work is determined by the fact that today the issue of high-quality pattern recognition is relevant, as is the construction of protection for such systems. The research methods of the thesis include the methods of systematic, comparative and statistical analysis, the logical-dialectical method of cognition, synthetic and expert evaluations, the method of logical generalization and synthesis. They are based on the use of methods of statistical qualitative and quantitative comparison, scientific abstraction, factor and structural analysis. A wide range of foreign and domestic literary and electronic sources were used. The scientific novelty of the obtained research results is that, based on the theoretical and methodological analysis, a model of adversarial attacks on pattern recognition systems was built and their vulnerabilities were shown, which can be used to improve protection systems. The practical application is that the results of the work can be used to build secure pattern recognition models in various institutions

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset

This paper aims to determine which is the best human action recognition method based on features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the papers that make reference to MSR Action3D, the most used dataset that includes depth information acquired from a RGB-D device, has been performed. We found that the validation method used by each work differs from the others. So, a direct comparison among works cannot be made. However, almost all the works present their results comparing them without taking into account this issue. Therefore, we present different rankings according to the methodology used for the validation in orden to clarify the existing confusion.Comment: 16 pages and 7 table

Robust similarity registration technique for volumetric shapes represented by characteristic functions

This paper proposes a novel similarity registration technique for volumetric shapes implicitly represented by their characteristic functions (CFs). Here, the calculation of rotation parameters is considered as a spherical crosscorrelation problem and the solution is therefore found using the standard phase correlation technique facilitated by principal components analysis (PCA).Thus, fast Fourier transform (FFT) is employed to vastly improve efficiency and robustness. Geometric moments are then used for shape scale estimation which is independent from rotation and translation parameters. It is numericallydemonstrated that our registration method is able to handle shapes with various topologies and robust to noise and initial poses. Further validation of our method is performed by registering a lung database