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arXiv 2022年

作者： Fu, Biying Damer, Naser The Department of Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research Darmstadt64283 Germany The Department of Computer Science TU Darmstadt Darmstadt64283 Germany

Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous works touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique, but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. Our study looks intointra and inter-dataset detectability to evaluate the generalizability of such a detection concept on different morphing techniques and bona fide sources. Our final results point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalized MAD with a correct classification accuracy of over 70%. © 2022, CC BY.

关键词： Image quality

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Synthetic Data for Face Recognition: Current State and Future Prospects

arXiv

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arXiv 2023年

作者： Boutros, Fadi Struc, Vitomir Fierrez, Julian Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia School of Engineering Universidad Autonoma de Madrid Madrid Spain Department of Computer Science TU Darmstadt Darmstadt Germany

Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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On the Security of Content key Distribution Framework for DRM systems

On the Security of Content key Distribution Framework for DR...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2022

作者： Rewal, Purva Kasi, Abhijit Obaidat, Mohammad S. Mishra, Dheerendra Mishra, Ankita Hsiao, Kuei-Fang Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications Bhopal462003 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Govt Nehru PG College Department of Mathematics Agarmalwa465441 India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

After the advancement of digital and Internet technologies, digital content mugging has become a major concern for the multimedia business to deal with. To combat piracy, digital rights management (DRM) systems are implemented. In DRM systems, protocols are being developed to provide secure communication. In this paper, we calibrate a recently proposed authentication protocol for DRM systems to understand security and privacy challenges. The objective of this cryptanalysis is to get a broad perspective of authentication schemes, which would help us to design secure and efficient authentication schemes for DRM systems. In this present study, we perform cryptanalysis of authentication protocol for DRM systems using biometrics by Khan et al.'s. Their scheme supports efficient communication and gives a better solution for the digital right management system. However, we identified that their scheme has some serious security flaws. Based on our analysis, we present a comparative study in terms of execution cost and security attributes to establish a tradeoff between efficiency and security. © 2022 IEEE.

关键词： Authentication

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Deep Light Direction Reconstruction from single RGB images 29

Deep Light Direction Reconstruction from single RGB images

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29th International Conference in Central Europe on computer graphics, Visualization and computer Vision, WSCG 2021

作者： Miller, Markus Nischwitz, Alfred Westermann, Rüdiger Dept. of Computer Science and Mathematics University of Applied Sciences Munich Lothstr. 64 Bavaria MunichD-80335 Germany Chair of Computer Graphics and Visualization Technical University Munich Boltzmannstr. 3/II Bavaria GarchingD-85748 Germany

ISBN: (纸本)9788086943343

In augmented reality applications, consistent illumination between virtual and real objects is important for creating an immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of the virtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed a method to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using a modified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. To avoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introduced stereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the light source direction, our approach does not require depth information and thus does not rely on special red-green-blue-depth (RGB-D) images as input. © 2021 University of West Bohemia. All rights reserved.

关键词： Image reconstruction

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The Impact of Adversarial Attacks on Medical Imaging AI Systems 13

The Impact of Adversarial Attacks on Medical Imaging AI Syst...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jain, Alok Sangeeta, K. Swathi, B. Sadim, Mohd. Dwivedi, Shashi Prakash Albawi, Ali Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P. Bhopal India Lloyd Institute of Engineering & Technology Knowledge Park Ii Uttar Pradesh Greater Noida India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350305463

Bad actors threaten medical imaging AI systems' dependability and safety, affecting patient care and diagnosis accuracy. A novel defense against this expanding threat is adversarial defense via ensemble integration. Explainable Feature-Based Defense (XFBD), Adversarial Training with Transfer Learning (ATTL), and Robust Classifier Augmentation (RCA) are three innovative techniques that have never been combined. RCA enhances training with controlled aggression. This allows the model to distinguish authentic medical imaging data from altered sources. ATTL makes transfer-learning-taught models more resistant to topic-specific hostile approaches. XFBD simplifies the defensive process, helping us comprehend how the model picks and fights different methods. A comparison indicates that ADEI always outperforms tried-and-true approaches. ADEI outperforms typical approaches in accuracy, sensitivity, precision, stability, interpretability, private protection, and computing cost. Strong safeguards are needed as AI-powered healthcare research becomes increasingly popular. As a lighthouse, ADEI protects everything from attack. The math behind each section and how they work together to strengthen the system make it valuable. ADEI advances AI-assisted healthcare's future. Testing instruments must be accurate and dependable in this industry. © 2024 IEEE.

关键词： Medical imaging

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Exploring Cognitive Decline in Hypertension: A Deep Learning Approach to Meningeal Interleukin-17-Producing T Cells in Mice

Exploring Cognitive Decline in Hypertension: A Deep Learning...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Praveen Gupta, Priyanka Rajalakshmi, B. Nijhawan, Ginni Kumari, Neha Kyhoiesh, Hussein Ali Kadhim Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

A deep learning approach that targets meningeal interleukin-17-producing T cells is used to research cognitive loss in high blood pressure patients. A detailed ablation study examined and rated each aspect of our recommended procedure. Including cellular activities was crucial since excluding them reduced accuracy. These elements are crucial to high blood pressure-related cognitive impairment. The deep learning model's layers worked effectively together to uncover complicated data patterns, as shown by removing some layers. Model complexity and computer time trade-offs were found, which can aid future improvements. In conclusion, our deep learning approach appears to be a promising tool to study how elevated blood pressure affects brain function. The ablation investigation proves the procedure works and reveals key components' responsibilities. This allows for more modifications and breakthroughs. This research helps us understand brain illnesses, including high blood pressure-related memory loss. It also sets the framework for innovative diagnosis and treatment methods. This study's deep learning and immunology discoveries might revolutionize high blood pressure-related cognitive impairment research and treatment. © 2024 IEEE.

关键词： Blood pressure

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Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

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computers, Materials & Continua 2021年第6期67卷 3451-3461页

作者： Samreen Naeem Wali Khan Mashwani Aqib Ali M.Irfan Uddin Marwan Mahmoud Farrukh Jamal Christophe Chesneau Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Faculty of Applied Studies King Abdulaziz UniversityJeddah21577Saudi Arabia Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Mathematics Universitéde CaenLMNOCampus IIScience 3Caen14032France

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related *** dataset was collected in raw form,and was subjected to natural language processing by way of data *** variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in *** better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector *** that were obtained using a sampling approach were then used for data *** machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized *** results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

关键词： Machine learning exchange rate sentiment analysis linear discriminant analysis principal component analysis simple logistic

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A novel eXplainable AI agent for social interaction training of people with Autism Spectrum Disorder (ASD)

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Gupta, Prashant K. Mazumdar, Bireshwar Dass Mishra, Manmohan Chadha, Rubina Komaragiri, Rama S. School of Computer Science Engineering and Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Department of Computer Applications United Institute of Management A-31 UPSIDC Industrial Area Naini Chak Noor Uttar Pradesh Prayagraj 211010 India Psychology / Counselling The Neuroaid and Research Foundation (NRF) C-19 Pamposh Enclave Delhi New Delhi 110048 India School of Engineering Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Greater Noida 203201 India

Autism Spectrum Disorder (ASD) is a neurological disorder that impacts a subject’s ability to be involved in a social interaction. Machine learning (ML) and deep learning (DL) models have been mainly used for ASD detection, which are however criticized for their ‘black-boxness’, advocating the use of eXplainable artificial intelligence (XAI) algorithms. As per the literature search so far, no social interaction model-based eXplainable Autism Spectrum Disorder (ASD) care agent exists for social interaction training of an ASD subject. Therefore, this work makes a beginning in this direction. A novel architecture of an eXplainable ASD care agent for social interaction training of ASD subjects, with an underlying intelligent AI algorithm is presented. Its working is elucidated through six representative social interaction scenarios and some practical considerations are also presented when the agent is put in practice with the ASD subject. The working of the proposed ASD care agent is also depicted through a suitable case study. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： AI Agent Artificial intelligence (AI) Autism spectrum disorder EXplainable AI Neurological disorders Social interaction training

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MATE: Masked Autoencoders are Online 3D Test-Time Learners

MATE: Masked Autoencoders are Online 3D Test-Time Learners

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International Conference on computer Vision (ICCV)

作者： M. Jehanzeb Mirza Inkyu Shin Wei Lin Andreas Schriebl Kunyang Sun Jaesung Choe Mateusz Kozinski Horst Possegger In So Kweon Kuk-Jin Yoon Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Korea Advanced Institute of Science and Technology (KAIST) South Korea Southeast University China

Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: a large portion of each test point cloud is removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. We show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, making it extremely lightweight. Our experiments show that MATE also achieves competitive performance by adapting sparsely on the test data, which further reduces its computational overhead, making it ideal for real-time applications.

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Machine Learning-Driven Blockchain for Enhanced Drug Discovery and Development in Pharmaceutical Research

Machine Learning-Driven Blockchain for Enhanced Drug Discove...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Asha, V. Anjimoon, Shaik Verma, Meenu Rani Singla, Atul Khan, Irfan Alkhafaji, Mohammed Ayad New Horizon College of Engineering Department of Computer Applications Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India GyanGanga Institute of Technology and Sciences DoCSBS M.P Jabalpur India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Machine learning (ML) and blockchain technologies have made huge steps forward in the pharmacy business by being used in the process of creating and discovering new medicines. Both breakthroughs were made possible by using both tools together. With the help of a blockchain design powered by machine learning, the study's main goal is to come up with a new way to make drug research more effective and efficient. This method makes it possible to make molecules with a wide range of structure features that have the right pharmacological qualities. You can also make these molecules, improve the plans of clinical trials, and guess how drugs will interact with their targets. Technologies like generative adversarial networks, reinforcement learning methods, and deep learning algorithms have made it possible for these things to be used. If blockchain technology is used throughout the whole process of making medicines, it improves global openness, traceability, and data security. This is because it makes sure that managing data is safe. The tests show that the suggested method works better than the others when it comes to the process of finding and developing new drugs. This is better because the creation process is more precise, accurate, and efficient. © 2024 IEEE.

关键词： Drug discovery

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