454 research outputs found
Attacking and Defending Printer Source Attribution Classifiers in the Physical Domain
The security of machine learning classifiers has received increasing attention in the last years. In forensic applications, guaranteeing
the security of the tools investigators rely on is crucial, since the gathered evidence may be used to decide about the innocence or the guilt
of a suspect. Several adversarial attacks were proposed to assess such
security, with a few works focusing on transferring such attacks from the
digital to the physical domain. In this work, we focus on physical domain
attacks against source attribution of printed documents. We first show
how a simple reprinting attack may be sufficient to fool a model trained
on images that were printed and scanned only once. Then, we propose
a hardened version of the classifier trained on the reprinted attacked
images. Finally, we attack the hardened classifier with several attacks,
including a new attack based on the Expectation Over Transformation
approach, which finds the adversarial perturbations by simulating the
physical transformations occurring when the image attacked in the digital domain is printed again. The results we got demonstrate a good
capability of the hardened classifier to resist attacks carried out in the
physical domai
Information embedding and retrieval in 3D printed objects
Deep learning and convolutional neural networks have become the main tools of computer vision. These techniques are good at using supervised learning to learn complex representations from data. In particular, under limited settings, the image recognition model now performs better than the human baseline. However, computer vision science aims to build machines that can see. It requires the model to be able to extract more valuable information from images and videos than recognition. Generally, it is much more challenging to apply these deep learning models from recognition to other problems in computer vision.
This thesis presents end-to-end deep learning architectures for a new computer vision field: watermark retrieval from 3D printed objects. As it is a new area, there is no state-of-the-art on many challenging benchmarks. Hence, we first define the problems and introduce the traditional approach, Local Binary Pattern method, to set our baseline for further study. Our neural networks seem useful but straightfor- ward, which outperform traditional approaches. What is more, these networks have good generalization. However, because our research field is new, the problems we face are not only various unpredictable parameters but also limited and low-quality training data.
To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the image segmentation area, and (ii) we cannot know everything from data, our models should be aware what key features they
should learn. This thesis explores these ideas and even explore more. We show how to use end-to-end deep learning models to learn to retrieve watermark bumps and tackle covariates from a few training images data. Secondly, we introduce ideas from synthetic image data and domain randomization to augment training data and understand various covariates that may affect retrieve real-world 3D watermark bumps. We also show how the illumination in synthetic images data to effect and even improve retrieval accuracy for real-world recognization applications
Image and Video Forensics
Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity
Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review
Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method
Modular framework for a breast biopsy smart navigation system
Dissertação de mestrado em Informatics EngineeringBreast cancer is currently one of the most commonly diagnosed cancers and the fifth leading cause of
cancer-related deaths. Its treatment has a higher survivorship rate when diagnosed in the disease’s early
stages. The screening procedure uses medical imaging techniques, such as mammography or ultrasound,
to discover possible lesions. When a physician finds a lesion that is likely to be malignant, a biopsy
is performed to obtain a sample and determine its characteristics. Currently, real-time ultrasound is the
preferred medical imaging modality to perform this procedure. The breast biopsy procedure is highly reliant
on the operator’s skill and experience, due to the difficulty in interpreting ultrasound images and correctly
aiming the needle. Robotic solutions, and the usage of automatic lesion segmentation in ultrasound
imaging along with advanced visualization techniques, such as augmented reality, can potentially make
this process simpler, safer, and faster.
The OncoNavigator project, in which this dissertation integrates, aims to improve the precision of
the current breast cancer interventions. To accomplish this objective various medical training and robotic
biopsy aid were developed. An augmented reality ultrasound training solution was created and the device’s
tracking capabilities were validated by comparing it with an electromagnetic tracking device. Another
solution for ultrasound-guided breast biopsy assisted with augmented reality was developed. This solution
displays real-time ultrasound video, automatic lesion segmentation, and biopsy needle trajectory display
in the user’s field of view. The validation of this solution was made by comparing its usability with the
traditional procedure. A modular software framework was also developed that focuses on the integration
of a collaborative medical robot with real-time ultrasound imaging and automatic lesion segmentation.
Overall, the developed solutions offered good results. The augmented reality glasses tracking capabilities
proved to be as capable as the electromagnetic system, and the augmented reality assisted breast biopsy
proved to make the procedure more accurate and precise than the traditional system.O cancro da mama é, atualmente, um dos tipos de cancro mais comuns a serem diagnosticados e a
quinta principal causa de mortes relacionadas ao cancro. O seu tratamento tem maior taxa de sobrevivência
quando é diagnosticado nas fases iniciais da doença. O procedimento de triagem utiliza técnicas
de imagem médica, como mamografia ou ultrassom, para descobrir possíveis lesões. Quando um médico
encontra uma lesão com probabilidade de ser maligna, é realizada uma biópsia para obter uma amostra
e determinar as suas características. O ultrassom em tempo real é a modalidade de imagem médica
preferida para realizar esse procedimento. A biópsia mamária depende da habilidade e experiência do
operador, devido à dificuldade de interpretação das imagens ultrassonográficas e ao direcionamento correto
da agulha. Soluções robóticas, com o uso de segmentação automática de lesões em imagens de
ultrassom, juntamente com técnicas avançadas de visualização, nomeadamente realidade aumentada,
podem tornar esse processo mais simples, seguro e rápido.
O projeto OncoNavigator, que esta dissertação integra, visa melhorar a precisão das atuais intervenções
ao cancro da mama. Para atingir este objetivo, vários ajudas para treino médico e auxílio à biópsia
por meio robótico foram desenvolvidas. Uma solução de treino de ultrassom com realidade aumentada
foi criada e os recursos de rastreio do dispositivo foram validados comparando-os com um dispositivo
eletromagnético. Outra solução para biópsia de mama guiada por ultrassom assistida com realidade aumentada
foi desenvolvida. Esta solução exibe vídeo de ultrassom em tempo real, segmentação automática
de lesões e exibição da trajetória da agulha de biópsia no campo de visão do utilizador. A validação desta
solução foi feita comparando a sua usabilidade com o procedimento tradicional. Também foi desenvolvida
uma estrutura de software modular que se concentra na integração de um robô médico colaborativo com
imagens de ultrassom em tempo real e segmentação automática de lesões. Os recursos de rastreio dos
óculos de realidade aumentada mostraram-se tão capazes quanto o sistema eletromagnético, e a biópsia
de mama assistida por realidade aumentada provou tornar o procedimento mais exato e preciso do que
o sistema tradicional
An investigation into applications of canonical polyadic decomposition & ensemble learning in forecasting thermal data streams in direct laser deposition processes
Additive manufacturing (AM) is a process of creating objects from 3D model data by adding layers of material. AM technologies present several advantages compared to traditional manufacturing technologies, such as producing less material waste and being capable of producing parts with greater geometric complexity. However, deficiencies in the printing process due to high process uncertainty can affect the microstructural properties of a fabricated part leading to defects. In metal AM, previous studies have linked defects in parts with melt pool temperature fluctuations, with the size of the melt pool and the scan pattern being key factors associated with part defects. Thus being able to adjust certain process parameters during a part\u27s fabrication, and knowing when to adjust these parameters, is critical to producing reliable parts. To know when to effectively adjust these parameters it is necessary to have models that can both identify when a defect has occurred and forecast the behavior of the process to identify if a defect will occur. This study focuses on the development of accurate forecasting models of the melt pool temperature distribution. Researchers at Mississippi State University have collected in-situ pyrometer data of a direct laser deposition process which captures the temperature distribution of the melt pool. The high-dimensionality and noise of the data pose unique challenges in developing accurate forecasting models. To overcome these challenges, a tensor decomposition modeling framework is developed that can actively learn and adapt to new data. The framework is evaluated on two datasets which demonstrates its ability to generate accurate forecasts and adjust to new data
The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021
The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners.
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The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies
- Technology and Application of Additive Manufacturing
- Digitalization of Industrial Production (Industry 4.0)
- Advances in the field of Cyber-Physical Systems
- Virtual and Augmented Reality Technologies throughout the entire product Life Cycle
- Human-machine-environment interaction
- Management and life cycle assessmen
- …