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Online database for secure data collection
Kopec, Peter ; Mezina, Anzhelika (oponent) ; Mikulec, Marek (vedoucí práce)
This bachelor thesis deals with the design and implementation of a secure online database for data collection, which is accessible from the Internet. A database that is accessible from the Internet and contains personal data or other valuable data must be well secured, because we do not want this data to be misused by an unauthorized person. To begin with, we select the appropriate applications for our system and analyze their functionality. The applications are selected based on the features they provide, the overall complexity and support of their online community. Part of the work is devoted to the analysis of data leaks from medical facilities in 2019 and 2020 and a few other leaks from other industries. Thanks to this analysis, we know the reasons for the data leakage and we are able to focus more on these weaknesses and point out the problems. The next part of the work is devoted to the design and implementation of a practical solution using applications that we selected at the beginning. In our case it is a MYSQL database, FLASK backend with Gunicorn WSGI and NGINX web server. Finally, we analyze the security of this solution using the most common vulnerabilities according to OWASP and the NMAP network scanner.
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Classification of thorax diseases on chest X-ray images using artificial intelligence
Pijáček, Štěpán ; Mikulec, Marek (oponent) ; Mezina, Anzhelika (vedoucí práce)
This thesis is researching workable solutions to the problem of classification of thorax disease on chest x-ray images using artificial intelligence. For a better understanding of the problem, the first chapters explain the basic convolutional neural network and its advantages and disadvantages. Based on these first explanations, two neural networks which are expanding on the concept of the convolutional neural network are chosen. Those are capsulated network and residual network both explained further in their respective sections with their advantages and disadvantages. Residual network and Capsulated network are implemented using programming language python and framework TensorFlow with Keras library, both with their respective chapters. At the end of this thesis, you can find results and conclusion.
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Face superresolution from image sequence
Mezina, Anzhelika ; Rajnoha, Martin (oponent) ; Burget, Radim (vedoucí práce)
This work is focused on application of deep learning in increasing resolution of images containing face. This can be applied in different fields, including security. For example, in case of incident, the police needs to identify a culprit from the records of security camera. The aim of this work is to propose neural network models, which would work with sequence of frames, and to compare these models with existing methods for a single image super-resolution. For this purpose, a new dataset with sequences of the images with faces is created. The methods for the single super-resolution are trained on the new dataset. The new architectures for multiframe super-resolution are proposed. They are based on U-Net model. This model is successful for segmentation tasks, but it can be also applied for super-resolution tasks. To improve this architecture, the residual blocks and its modification are used. To avoid blurring effect and recover more details, the perceptual loss function is applied. In the first part of this work, the description of neural networks and overview of the architectures, which can be applied in super-resolution, is provided. The second part contains the methods for super-resolution of a single frame, multiframe, video. In the next section, there is a description of proposed architectures and description of the experiment. In the last part of the work, multiframe methods and single frame methods are compared. In the result, the proposed methods recover more details, however, some architectures produce artefacts, which can be reduced using a filter, for example, Gaussian. New methods allow to reduce the number of failed face recognition. This fact is necessary for person identification in case of incidents.
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Increasing quality of facial images using sequence of images
Svorad, Adam ; Mezina, Anzhelika (oponent) ; Burget, Radim (vedoucí práce)
Master’s thesis delves into the field of face super-resolution. It aims to review novel approaches to single-frame image sharpening and image editing in the theoretical part of the work. Practical part will focus on approaches to image reconstruction from a sequence of damaged images. Multiple multi-frame neural network models will be implemented and evaluated. As alternative option, a suite of image editing tools will be presented as well. These tools will utilize most modern image editing techniques to merge visual features of faces from multiple input images into a single output image. At the end of the thesis, all methods will be compared to each other.
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Protection of sensitive data contained in images
Mezina, Anzhelika ; Rajnoha, Martin (oponent) ; Burget, Radim (vedoucí práce)
This work is focused on application of deep learning in security problem of escape sensitive information, that is contained in images. The presented solution of this problem is using Single Shot Multibox Detector and Fully Connected Network (FCN). FCN is faster than other methods and can be applied in industry, where is a need to analyse input and output information very quickly, for example, in network traffic analysis. In the first part of this work, methods that can be used in keyword detection are described. The second part contains a description of experiment and achieved results for two models of neural network: Single Shot Multibox Detector and Fully Connected Network. The second one gave better results and can be used in practice.
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Chest X-ray Image Analysis using Convolutional Vision Transformer
Mezina, Anzhelika ; Burget, Radim
In recent years, computer techniques for clinical imageanalysis have been improved significantly, especially becauseof the pandemic situation. Most recent approaches are focusedon the detection of viral pneumonia or COVID-19 diseases.However, there is less attention to common pulmonary diseases,such as fibrosis, infiltration and others. This paper introduces theneural network, which is aimed to detect 14 pulmonary diseases.This model is composed of two branches: global, which is theInceptionNetV3, and local, which consists of Inception modulesand a modified Vision Transformer. Additionally, the AsymmetricLoss function was utilized to deal with the problem of multilabelclassification. The proposed model has achieved an AUC of 0.8012and an accuracy of 0.7429, which outperforms the well-knownclassification models.
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Analýza rentgenových snímků za účelem odstranění rušivých artefaktů v bezpečnostních aplikacích
Schiller, Vojtěch ; Mezina, Anzhelika (oponent) ; Burget, Radim (vedoucí práce)
Práce se zabývá problematikou dekompozice složeného rentgenového obrazu, na kterém jsou společně přítomny jak klíčové informační složky, tak složky šumu. Cílem je pomocí technik hlubokého učení přítomné rušivé artefakty ve formě opakujících se jevů na pozadí odstranit, přičemž klást důraz na precizní zachování informačních složek obsažených v obraze. K docílení byla použita konvoluční neuronová síť U-Net a její vylepšené varianty, které dominují především v odvětví segmentace obrazu. Společně byly také natrénovány a porovnány konkurenční modely dosahující výborných výsledků odstraňování šumu z obrazu. Práce představuje novou metodu, která byla srovnána s nejmodernějšími architekturami a ve výsledcích objektivně i subjektivně významně překonala na stejné datové sadě všechny porovnávané.
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Demonstrace kryptografických problémů formou interaktivní vzdělávací hry
Fišarová, Anežka ; Mezina, Anzhelika (oponent) ; Mikulec, Marek (vedoucí práce)
Bakalářská práce je zaměřená na návrh a realizaci kryptografické interaktivní vzdělávací hry. Věnuje se různým kryptografickým šifrám vybraných tak, aby i člověka mimo obor mohly seznámit s danou problematikou názornou a zábavnou formou. Jako programovací jazyk byla použita Java a JavaFX. Práce se sestává ze čtyř částí, z nichž první dvě jsou orientované na teoretickou část kryptografie a druhé dvě na praktické využití a vývoj v kódu. Jako motivace pro hráče bylo využito bodové ohodnocení za správně vyluštěné šifry. Dále práce obsahuje volbu obtížnosti, která má vliv na to, jaké šifry budou hráči prezentovány. Na závěr práce autor reflektuje nad výsledky a zkoumá možnosti dalšího vylepšení uvedených metod.
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Superrozlišení v obraze pro zajištění vylepšeného monitorování zabezpečených prostorů
Rosa, Martin ; Mezina, Anzhelika (oponent) ; Burget, Radim (vedoucí práce)
Cieľom bakalárskej práce bolo porovnať modely super-rozlíšenia s aplikáciou na reštaurovanie fotiek ľudských tvárí. V práci sme spracovali rešerš technológií superrozlíšenia a následne sme natrénovali a porovnali 5 modelov. Zameriavame sa hlavne na oblasť superrozlíšenia, ktorá by mohla byť nápomocná na identifikáciu osôb z bezpečnostných kamier. Použité technológie boli preto vyberané na základe percepčnej kvality a schopnosti identifikácie osoby na výstupnom snímku. Práca ukázala účinnosť porovnaných modelov pomocou objektívnych aj subjektívnych metrík. Výsledky boli porovnané v dotazníku (106 respondentov). Dotazník ukázal účinnosť použitia vlnovej transformácie v superrozlíšení tvarí.
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Classification of thorax diseases on chest X-ray images using artificial intelligence
Pijáček, Štěpán ; Mikulec, Marek (oponent) ; Mezina, Anzhelika (vedoucí práce)
This thesis is researching workable solutions to the problem of classification of thorax disease on chest x-ray images using artificial intelligence. For a better understanding of the problem, the first chapters explain the basic convolutional neural network and its advantages and disadvantages. Based on these first explanations, two neural networks which are expanding on the concept of the convolutional neural network are chosen. Those are capsulated network and residual network both explained further in their respective sections with their advantages and disadvantages. Residual network and Capsulated network are implemented using programming language python and framework TensorFlow with Keras library, both with their respective chapters. At the end of this thesis, you can find results and conclusion.
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