Implementation of Deep Learning models for Information Extraction on Identification Documents

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe development of object detection models has revolutionized the analysis of personal information on identification cards, leading to a decrease in external human labor. Although previous strategies have been employed to address this issue without using machine learning models, they all present certain limitations, which artificial intelligence aims to overcome. This report delves into the development of a deep learning-based object detection capable of recognizing relevant information from Portuguese identification cards. All the decisions made during the project will be accompanied by a detailed background theory. Additionally, we provide an in-depth analysis of Optical Character Recognition (OCR) technology, which was utilized throughout the project to generate text from images. As the newest member of the Machine learning Team of Biometrid, I had the privilege of being involved in this project that led to the improvement of the current approach that does not leverage machine learning in the detection of relevant sections from ID cards. The findings of this project provide a foundation for further research into the use of AI in identification card analysis

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Personalizing Human-Robot Dialogue Interactions using Face and Name Recognition

Task-oriented dialogue systems are computer systems that aim to provide an interaction indistinguishable from ordinary human conversation with the goal of completing user- defined tasks. They are achieving this by analyzing the intents of users and choosing respective responses. Recent studies show that by personalizing the conversations with this systems one can positevely affect their perception and long-term acceptance. Personalised social robots have been widely applied in different fields to provide assistance. In this thesis we are working on development of a scientific conference assistant. The goal of this assistant is to provide the conference participants with conference information and inform about the activities for their spare time during conference. Moreover, to increase the engagement with the robot our team has worked on personalizing the human-robot interaction by means of face and name recognition. To achieve this personalisation, first the name recognition ability of available physical robot was improved, next by the concent of the participants their pictures were taken and used for memorization of returning users. As acquiring the consent for personal data storage is not an optimal solution, an alternative method for participants recognition using QR Codes on their badges was developed and compared to pre-trained model in terms of speed. Lastly, the personal details of each participant, as unviversity, country of origin, was acquired prior to conference or during the conversation and used in dialogues. The developed robot, called DAGFINN was displayed at two conferences happened this year in Stavanger, where the first time installment did not involve personalization feature. Hence, we conclude this thesis by discussing the influence of personalisation on dialogues with the robot and participants satisfaction with developed social robot

Automating Intersection Marking Data Collection and Condition Assessment at Scale With An Artificial Intelligence-Powered System

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance

Understanding and controlling leakage in machine learning

Machine learning models are being increasingly adopted in a variety of real-world scenarios. However, the privacy and confidentiality implications introduced in these scenarios are not well understood. Towards better understanding such implications, we focus on scenarios involving interactions between numerous parties prior to, during, and after training relevant models. Central to these interactions is sharing information for a purpose e.g., contributing data samples towards a dataset, returning predictions via an API. This thesis takes a step toward understanding and controlling leakage of private information during such interactions. In the first part of the thesis we investigate leakage of private information in visual data and specifically, photos representative of content shared on social networks. There is a long line of work to tackle leakage of personally identifiable information in social photos, especially using face- and body-level visual cues. However, we argue this presents only a narrow perspective as images reveal a wide spectrum of multimodal private information (e.g., disabilities, name-tags). Consequently, we work towards a Visual Privacy Advisor that aims to holistically identify and mitigate private risks when sharing social photos. In the second part, we address leakage during training of ML models. We observe learning algorithms are being increasingly used to train models on rich decentralized datasets e.g., personal data on numerous mobile devices. In such cases, information in the form of high-dimensional model parameter updates are anonymously aggregated from participating individuals. However, we find that the updates encode sufficient identifiable information and allows them to be linked back to participating individuals. We additionally propose methods to mitigate this leakage while maintaining high utility of the updates. In the third part, we discuss leakage of confidential information during inference time of black-box models. In particular, we find models lend themselves to model functionality stealing attacks: an adversary can interact with the black-box model towards creating a replica `knock-off' model that exhibits similar test-set performances. As such attacks pose a severe threat to the intellectual property of the model owner, we also work towards effective defenses. Our defense strategy by introducing bounded and controlled perturbations to predictions can significantly amplify model stealing attackers' error rates. In summary, this thesis advances understanding of privacy leakage when information is shared in raw visual forms, during training of models, and at inference time when deployed as black-boxes. In each of the cases, we further propose techniques to mitigate leakage of information to enable wide-spread adoption of techniques in real-world scenarios.Modelle für maschinelles Lernen werden zunehmend in einer Vielzahl realer Szenarien eingesetzt. Die in diesen Szenarien vorgestellten Auswirkungen auf Datenschutz und Vertraulichkeit wurden jedoch nicht vollständig untersucht. Um solche Implikationen besser zu verstehen, konzentrieren wir uns auf Szenarien, die Interaktionen zwischen mehreren Parteien vor, während und nach dem Training relevanter Modelle beinhalten. Das Teilen von Informationen für einen Zweck, z. B. das Einbringen von Datenproben in einen Datensatz oder die Rückgabe von Vorhersagen über eine API, ist zentral für diese Interaktionen. Diese Arbeit verhilft zu einem besseren Verständnis und zur Kontrolle des Verlusts privater Informationen während solcher Interaktionen. Im ersten Teil dieser Arbeit untersuchen wir den Verlust privater Informationen bei visuellen Daten und insbesondere bei Fotos, die für Inhalte repräsentativ sind, die in sozialen Netzwerken geteilt werden. Es gibt eine lange Reihe von Arbeiten, die das Problem des Verlustes persönlich identifizierbarer Informationen in sozialen Fotos angehen, insbesondere mithilfe visueller Hinweise auf Gesichts- und Körperebene. Wir argumentieren jedoch, dass dies nur eine enge Perspektive darstellt, da Bilder ein breites Spektrum multimodaler privater Informationen (z. B. Behinderungen, Namensschilder) offenbaren. Aus diesem Grund arbeiten wir auf einen Visual Privacy Advisor hin, der darauf abzielt, private Risiken beim Teilen sozialer Fotos ganzheitlich zu identifizieren und zu minimieren. Im zweiten Teil befassen wir uns mit Datenverlusten während des Trainings von ML-Modellen. Wir beobachten, dass zunehmend Lernalgorithmen verwendet werden, um Modelle auf umfangreichen dezentralen Datensätzen zu trainieren, z. B. persönlichen Daten auf zahlreichen Mobilgeräten. In solchen Fällen werden Informationen von teilnehmenden Personen in Form von hochdimensionalen Modellparameteraktualisierungen anonym verbunden. Wir stellen jedoch fest, dass die Aktualisierungen ausreichend identifizierbare Informationen codieren und es ermöglichen, sie mit teilnehmenden Personen zu verknüpfen. Wir schlagen zudem Methoden vor, um diesen Datenverlust zu verringern und gleichzeitig die hohe Nützlichkeit der Aktualisierungen zu erhalten. Im dritten Teil diskutieren wir den Verlust vertraulicher Informationen während der Inferenzzeit von Black-Box-Modellen. Insbesondere finden wir, dass sich Modelle für die Entwicklung von Angriffen, die auf Funktionalitätsdiebstahl abzielen, eignen: Ein Gegner kann mit dem Black-Box-Modell interagieren, um ein Replikat-Knock-Off-Modell zu erstellen, das ähnliche Test-Set-Leistungen aufweist. Da solche Angriffe eine ernsthafte Bedrohung für das geistige Eigentum des Modellbesitzers darstellen, arbeiten wir auch an einer wirksamen Verteidigung. Unsere Verteidigungsstrategie durch die Einführung begrenzter und kontrollierter Störungen in Vorhersagen kann die Fehlerraten von Modelldiebstahlangriffen erheblich verbessern. Zusammenfassend lässt sich sagen, dass diese Arbeit das Verständnis von Datenschutzverlusten beim Informationsaustausch verbessert, sei es bei rohen visuellen Formen, während des Trainings von Modellen oder während der Inferenzzeit von Black-Box-Modellen. In jedem Fall schlagen wir ferner Techniken zur Verringerung des Informationsverlusts vor, um eine weit verbreitete Anwendung von Techniken in realen Szenarien zu ermöglichen.Max Planck Institute for Informatic