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2025 IEEE/CVF Winter Conference on Applications of computer Vision Workshops, WACVW 2025

作者： Huber, Marco Neto, Pedro C. Sequeira, Ana F. Damer, Naser Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Technische Universität Darmstadt Department of Computer Science Germany Inesc Tec Porto Portugal University of Porto Faculry of Engineering Portugal

ISBN: (纸本)9798331536626

Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner. © 2025 IEEE.

关键词： Frequency domain analysis

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Text-dependent Speaker Verification Challenge 2024: Exploring Shared and User-defined Passphrases

Text-dependent Speaker Verification Challenge 2024: Explorin...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zeinali, Hossein Lee, Kong Aik Alam, Jahangir Burget, Lukáš Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Department of Computer Graphics and Multimedia Brno University of Technology Brno Czech Republic Computer Research Institute of Montreal Montreal Canada

ISBN: (纸本)9798350368741

In contrast to text-independent speaker verification, which has received significant attention from researchers and has many competitions dedicated to it, text-dependent speaker verification (TdSV) has been less explored recently. The TdSV Challenge 2024 was organized to analyze and explore novel methods for this type of speaker verification and aims to motivate participants to develop new approaches to TdSV, conduct comprehensive analyses, and investigate advanced techniques such as self-supervised learning. This challenge builds on the achievements of the short-duration speaker verification (SdSV) Challenges held in 2020 and 2021 and focuses specifically on TdSV in two distinct scenarios. The first scenario involves conventional TdSV, while the second focuses on speaker enrollment using user-defined passphrases. This paper provides a detailed description of both tasks, introduces the evaluation rules, and presents a comprehensive analysis of the results obtained from this challenge. © 2025 IEEE.

关键词： DeepMine dataset TdSV Challenge Text-dependent Speaker Verification User-defined passphrase

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Time Series Forecasting of Stock Prices Using Neural Networks LSTM and GAN 1

Time Series Forecasting of Stock Prices Using Neural Network...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Mane, Vijay Patil, Ruturaj Pawar, Shantanu Pujari, Nagesh Jaggi, Arjun Kakkar, Preetish Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune Pune India Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India HCL Tech CA United States Senior Computer Graphics Engineer at Adobe SeattleWA United States

ISBN: (纸本)9798350355611

The project seeks to improve the precision of Amazon stock price prediction using advanced neural network techniques, specifically Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs). The analysis is based on OHLCV (Open, High, Low, Close, Volume) data, and various models were employed, including both conventional statistical methods like ARIMA and Fourier Transform models, as well as cutting-edge deep learning approaches. Feature extraction plays a critical role in improving model performance, with 19 features being created, such as momentum, Bollinger Bands, moving averages, and exponential moving averages (EMA), which significantly improved the accuracy of predictions. While ARIMA and Fourier Transform models yielded precise results with low AIC, BIC values, and close p-values, LSTM models faced challenges in short-term price prediction. Despite utilizing both single and multiple features, LSTMs were more effective in forecasting long-term trends rather than capturing short-term price fluctuations. Sentiment analysis from news and Reddit data is also incorporated to further refine stock price predictions. In addition to LSTMs, GANs were utilized to generate synthetic stock data for further training the models, where the historical trends are created using a Generator, and CNN acts as the Discriminator. By clubbing the advantages of neural networks for processing sequential data and also the use of GANs for generating realistic stock data, this unique method aims to increase the overall model accuracy. © 2025 IEEE.

关键词： Generative adversarial networks

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SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning

arXiv

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arXiv 2025年

作者： Ivanovska, Marija Todorov, Leon Damer, Naser Jain, Deepak Kumar Peer, Peter Štruc, Vitomir Faculty of Electrical Engineering University in Ljubljana Slovenia Faculty of Computer and Information Science University in Ljubljana Slovenia Dalian University of Technology China Fraunhofer Institute for Computer Graphics Research Germany

With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious activities. Contemporary Morphing Attack Detection (MAD) approaches frequently rely on supervised, discriminative models trained on examples of bona fide and morphed images. These models typically perform well with morphs generated with techniques seen during training, but often lead to sub-optimal performance when subjected to novel unseen morphing techniques. While unsupervised models have been shown to perform better in terms of generalizability, they typically result in higher error rates, as they struggle to effectively capture features of subtle artifacts. To address these shortcomings, we present SelfMAD, a novel self-supervised approach that simulates general morphing attack artifacts, allowing classifiers to learn generic and robust decision boundaries without overfitting to the specific artifacts induced by particular face morphing methods. Through extensive experiments on widely used datasets, we demonstrate that SelfMAD significantly outperforms current state-of-the-art MADs, reducing the detection error by more than 64% in terms of EER when compared to the strongest unsupervised competitor, and by more than 66%, when compared to the best performing discriminative MAD model, tested in cross-morph settings. The source code for SelfMAD is available at https://***/LeonTodorov/SelfMAD. © 2025, CC BY-NC-ND.

关键词： Self-supervised learning

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FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks

FX-MAD: Frequency-Domain Explainability and Explainability-D...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Marco Huber Pedro C. Neto Ana F. Sequeira Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Germany INESC TEC Porto Portugal Faculry of Engineering University of Porto Portugal

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

关键词： computer vision Correlation Face recognition Soft sensors Frequency-domain analysis Conferences Benchmark testing Security Facial features

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Text-dependent Speaker Verification Challenge 2024: Exploring Shared and User-defined Passphrases

Text-dependent Speaker Verification Challenge 2024: Explorin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hossein Zeinali Kong Aik Lee Jahangir Alam Lukáš Burget Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Computer Research Institute of Montreal Montreal Canada Department of Computer Graphics and Multimedia Brno University of Technology Brno Czech Republic

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Self-supervised learning Organizations Signal processing Acoustics Speaker recognition Speech processing

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To the Point: From Dynamic Heatmap Video to Gaze Points 25

To the Point: From Dynamic Heatmap Video to Gaze Points

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Beryl Gnanaraj Swetha Manivasagam Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Bangalore Karnataka India Department of Computer Science and Engineering PSG College of Technology Coimbatore Tamil Nadu India

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Time Series Forecasting of Stock Prices Using Neural Networks LSTM and GAN

Time Series Forecasting of Stock Prices Using Neural Network...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Vijay Mane Ruturaj Patil Shantanu Pawar Nagesh Pujari Arjun Jaggi Preetish Kakkar Electronics and Telecommunication Engineering. Vishwakarma Institute of Technology Pune Pune India Electronics and Telecommunication Engineering. Vishwakarma Institute of Technology Pune India HCL Tech California US Senior Computer Graphics Engineer at Adobe Seattle Washington United State

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

关键词： Analytical models Adaptation models Sentiment analysis Social networking (online) Neural networks Predictive models Generative adversarial networks Data models Forecasting Long short term memory

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Conflict detection and resolution for authorization policies in workflow systems

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2009年第8期10卷 1082-1092页

作者： Chen-hua MA Guo-dong LU Jiong QIU Engineering & Computer Graphics Institute Zhejiang University Hangzhou 310027 China Department of Computer Engineering Hangzhou Dianzi University Hangzhou 310008 China

The specification of authorization policies in access control models proposed so far cannot satisfy the requirements in workflow management systems(WFMSs).Furthermore,existing approaches have not provided effective conflict detection and resolution methods to maintain the consistency of authorization polices in *** address these concerns,we propose the definition of authorization policies in which context constraints are considered and the complicated requirements in WFMSs can be *** on the definition,we put forward static and dynamic conflict detection methods for authorization *** defining two new concepts,the precedence establishment rule and the conflict resolution policy,we provide a flexible approach to resolving conflicts.

关键词： Workflow management system （WFMS） Authorization policy Conflict detection and resolution

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