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arXiv 2022年

作者： Huber, Marco Boutros, Fadi Luu, Anh Thi Raja, Kiran Ramachandra, Raghavendra Damer, Naser Neto, Pedro C. Gonçalves, Tiago Sequeira, Ana F. Cardoso, Jaime S. Tremoço, João Lourenço, Miguel Serra, Sergio Cermeño, Eduardo Ivanovska, Marija Batagelj, Borut Kronovšek, Andrej Peer, Peter Štruc, Vitomir Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Norwegian University of Science and Technology Norway INESC TEC Portugal University of Porto Portugal Yoonik Portugal Vaelsys R&D Spain University of Ljubljana Slovenia

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people’s privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://***/marcohuber/SYN-MAD-2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Competition

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RAKI:A Robust ECC Based Three-party Authentication and Key Agreement Scheme for Medical IoT

RAKI:A Robust ECC Based Three-party Authentication and Key A...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yousheng Zhou Lunhao Li Mohammad S. Obaidat Yuanni Liu P. Vijayakumar Kuei-Fang Hsiao School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science and Engineering Indian Institute of Technology Dhanbad India King Abdullah II School of Information Technology University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computer Science & Engineering University College of Engineering Tindivanam Tindivanam India Information Systems Department University of Sharjah Shrajah UAE

ISBN: (纸本)9781665435413

With its advantages are gradually emerging, the Internet of Things (IoT) is profoundly changing the way people work and live. Among all IoT applications, medical IoT is partic-ularly important, in which the user can communicate with smart medical device through the hospital gateway node. However, due to the inherent defects of these smart devices and the openness of wireless networks, medical IoT is vulnerable to kinds of attacks, such as impersonation attack and password guessing attack. Unfortunately, there are few authentication schemes for medical IoT at present, while existing three-party schemes have various weakness and are not suitable for medical IoT. Given the sensitivity of patient data and the deadly consequences of attacks on medical devices, there is an urgent need to develop a suitable authentication scheme in Medical IoT Network with high security. To alleviate the above problems, a robust ECC based three-party authentication and key agreement scheme for medical IoT(RAKI) has been proposed, which is secure with random oracle model and the informal security analysis. Besides, the performance comparisons against existing competing three-party schemes indicate that our scheme is efficient for medical IoT.

关键词： Medical devices Sensitivity Protocols Wireless networks Authentication Resists Passwords

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The Impact of Adversarial Attacks on Medical Imaging AI Systems

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

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International Conference on Communication Systems and Network Technologies (CSNT)

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

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

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.

关键词： Training Sensitivity Computational modeling Transfer learning Stability analysis Security Artificial intelligence

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A CPW Loaded Quasi-T Shaped Planar Antenna for WLAN and X-Band Applications 3

A CPW Loaded Quasi-T Shaped Planar Antenna for WLAN and X-Ba...

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3rd URSI Atlantic and Asia Pacific Radio science Meeting, AT-AP-RASC 2022

作者： Raza Rizvi, Syed Naheel Zaidi, Abir Alibakhshikenari, Mohammad Dalarsson, Mariana Burokur, Shah Nawaz Chungbuk National University Department of Information and Communication Engineering Cheongju Korea Republic of Hassan Ii University Department of Electrical Engineering Faculty of Science and Techniques Casablanca Morocco Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Madrid28911 Spain School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm100-44 Sweden Leme Upl University Paris Nanterre Ville d'AvrayF92410 France

ISBN: (纸本)9789463968058

In this paper, a compact, geometrically simple, dual band and high gain coplanar waveguide (CPW) fed antenna for WLAN and X-band is proposed. The antenna is designed on Rogers RT/Duroid 5880 substrate with overall size of 25 mm × 33 mm × 0.79 mm. The proposed antenna consists of circular and rectangular stubs with one rectangular slot. The stubs and slots are introduced in order to achieve the desired bandwidth and high gain. The proposed antenna is designed and analyzed using the electromagnetic (EM) tool HFSS. Moreover, the proposed antenna design is compared to previously published work, and the comparison shows that our proposed antenna is a good candidate for radar applications. © 2022 URSI.

关键词： Coplanar waveguides

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FocusFace: Multi-task contrastive learning for masked face recognition

arXiv

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arXiv 2021年

作者： Neto, Pedro C. Boutros, Fadi Pinto, João Ribeiro Damer, Naser Sequeira, Ana F. Cardoso, Jaime S. INESC TEC Porto Portugal Faculdade de Engenharia Universidade Do Porto Porto Portugal Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (MM) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared *** further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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ID-Booth: Identity-consistent Face Generation with Diffusion Models

arXiv

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arXiv 2025年

作者： Tomašević, Darian Boutros, Fadi Lin, Chenhao Damer, Naser Štruc, Vitomir Peer, Peter University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an China University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia

Recent advances in generative modeling have enabled the generation of high-quality synthetic data that is applicable in a variety of domains, including face recognition. Here, state-of-the-art generative models typically rely on conditioning and fine-tuning of powerful pretrained diffusion models to facilitate the synthesis of realistic images of a desired identity. Yet, these models often do not consider the identity of subjects during training, leading to poor consistency between generated and intended identities. In contrast, methods that employ identity-based training objectives tend to overfit on various aspects of the identity, and in turn, lower the diversity of images that can be generated. To address these issues, we present in this paper a novel generative diffusion-based framework, called ID-Booth. ID-Booth consists of a denoising network responsible for data generation, a variational auto-encoder for mapping images to and from a lower-dimensional latent space and a text encoder that allows for prompt-based control over the generation procedure. The framework utilizes a novel triplet identity training objective and enables identity-consistent image generation while retaining the synthesis capabilities of pretrained diffusion models. Experiments with a state-of-the-art latent diffusion model and diverse prompts reveal that our method facilitates better intra-identity consistency and inter-identity separability than competing methods, while achieving higher image diversity. In turn, the produced data allows for effective augmentation of small-scale datasets and training of better-performing recognition models in a privacy-preserving manner. The source code for the ID-Booth framework is publicly available at https://***/dariant/ID-Booth. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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When are prime formulae characteristic?

arXiv

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arXiv 2024年

作者： Aceto, Luca Monica, Dario Della Fábregas, Ignacio Ingólfsdóttir, Anna L’Aquila Italy ICE-TCS School of Computer Science Reykjavik University Reykjavik Iceland Italy Departamento de Sistemas Informáticos y Computación Universidad Complutense de Madrid Madrid Spain Department of Electrical Engineering and Information Technology University of Naples "Federico II" Naples Italy IMDEA Software Institute Madrid Spain

In the setting of the modal logic that characterizes modal refinement over modal transition systems, Boudol and Larsen showed that the formulae for which model checking can be reduced to preorder checking, that is, the characteristic formulae, are exactly the consistent and prime ones. This paper presents general, sufficient conditions guaranteeing that characteristic formulae are exactly the consistent and prime ones. It is shown that the given conditions apply to various behavioural relations in the literature. In particular, characteristic formulae are exactly the prime and consistent ones for all the semantics in van Glabbeek’s linear time-branching time spectrum. © 2024, CC BY.

关键词： Model checking

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SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

SYN-MAD 2022: Competition on Face Morphing Attack Detection ...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Marco Huber Fadi Boutros Anh Thi Luu Kiran Raja Raghavendra Ramachandra Naser Damer Pedro C. Neto Tiago Gonçalves Ana F. Sequeira Jaime S. Cardoso João Tremoço Miguel Lourenço Sergio Serra Eduardo Cermeño Marija Ivanovska Borut Batagelj Andrej Kronovšek Peter Peer Vitomir Štruc Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Norwegian University of Science and Technology Norway INESC TEC Portugal University of Porto Portugal Yoonik Portugal Vaelsys R&D Spain University of Ljubljana Slovenia

ISBN: (纸本)9781665463959

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 In-ternational Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 differ-ent countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and at-tract solutions that deal with detecting face morphing at-tacks while protecting people's privacy for ethical and le-gal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to out-performing the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://***/marcohuber/SYN-MAD-2022.

关键词： Industries Technological innovation Privacy Ethics Data privacy Biometrics (access control) Law

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Artificial Neural Network using Image Processing for Digital Forensics Crime Scene Object Detection

Artificial Neural Network using Image Processing for Digital...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Deepa Devasenapathy M Raja Rajat Kumar Dwibedi N Vinoth T. Jayasudha V Dilli Ganesh Instructor-II/ Computing & Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Department of Computer Science and Engineeting CMR Institute of Technology Bengaluru India Aarupadai Veedu Institute of Technology VMRF Chennai India Department of Mechatronics Engineering Chennai Institute of Technology Chennai India Department of English KPR Institute of Engineering and Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-(SIMATS) Chennai India

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by Artificial Neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches.

关键词：

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A Multi-Level Thresholding Image Segmentation Based on an Improved Artificial Bee Colony Algorithm 2nd

A Multi-Level Thresholding Image Segmentation Based on an Im...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Image segmentation

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