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Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yin, Zhaoxia Zhu, Shaowei Su, Hang Peng, Jianteng Lyu, Wanli Luo, Bin East China Normal University School of Communication & Electronic Engineering Shanghai China Anhui University China Tsinghua University Department of Computer Science and Technology Beijing100084 China OPPO Intellisense and Interaction Research Department China

Deep Neural Networks (DNNs) have recently made significant strides in various fields;however, they are susceptible to adversarial examples-crafted inputs with imperceptible perturbations that can mislead these networks. Notably, even when adversaries lack access to the complete model parameters, they can still generate adversarial examples targeting a range of DNN-based task systems. Various defense mechanisms have been proposed, such as feature compression and gradient masking. Nevertheless, extensive research indicates that these methods often address only specific attacks, rendering them ineffective against novel and unknown attack strategies. Recent studies have highlighted the efficacy of identifying adversarial examples in the frequency domain;however, these approaches are limited to frequency-based analysis. In this study, we experimentally observe that adversarial examples possess significant characteristics in local regions. Specifically, adversarial perturbations exhibit localized randomness, whereas the high-frequency information in normal examples is both locally coherent and semantically relevant. This critical distinction enables effectively distinguishing adversarial examples from normal ones. To leverage this insight, we aim to enhance the high-frequency features of input examples to amplify their feature disparities. We propose an image enhancement method utilizing local histogram equalization. Our experimental results demonstrate that this method substantially improves detector performance without modifying the existing detection models. Furthermore, this technique can be seamlessly integrated with task models, effectively reducing deployment costs in practical applications. © 2004-2012 IEEE.

关键词： Frequency domain analysis

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Long-Term Effects of User Expertise and Application Design on Collision Anxiety in VR Games 25

Long-Term Effects of User Expertise and Application Design o...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Patrizia Ring Maic Masuch Faculty of Computer Science / Department of Human-centered Computing and Cognitive Science (HCCS) / Entertainment Computing Group University of Duisburg-Essen Duisburg Germany

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How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategies

arXiv

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arXiv 2025年

作者： Ran, Zihe Fan, Xianzhe Li, Xiyu Li, Franklin Mingzhe Lu, Zhicong Xiao, Qing Wang, Yanyun Communication University of China Beijing China Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science George Mason University FairfaxVA United States College of Media Communication and Information University of Colorado Boulder BoulderCO United States

As blind and low-vision (BLV) players engage more deeply with games, accessibility features have become essential. While some research has explored tools and strategies to enhance game accessibility, the specific experiences of these players with mobile games remain underexamined. This study addresses this gap by investigating how BLV users experience mobile games with varying accessibility levels. Through interviews with 32 experienced BLV mobile players, we explore their perceptions, challenges, and strategies for engaging with mobile games. Our findings reveal that BLV players turn to mobile games to alleviate boredom, achieve a sense of accomplishment, and build social connections, but face barriers depending on the game’s accessibility level. We also compare mobile games to other forms of gaming, highlighting the relative advantages of mobile games, such as the inherent accessibility of smartphones. This study contributes to understanding BLV mobile gaming experiences and provides insights for enhancing accessible mobile game design. Copyright © 2025, The Authors. All rights reserved.

关键词： Game design

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Optimizing elderly care: A data-driven AI model for predicting polypharmacy risk in the elderly using SHARE data

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Neuroscience 2025年 577卷 132-143页

作者： Elhosseiny, Aliaa A. Eldawlatly, Seif Ramadan, Eman Börsch-Supan, Axel Salama, Mohamed Institute of Global Health and Human Ecology (I-GHHE) The American University in Cairo Cairo Egypt Department of Pharmacology and Toxicology Faculty of Pharmacy The British University in Egypt Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt Computer and Systems Engineering Department Ain Shams University Cairo Egypt Survey of Health Ageing and Retirement in Europe (SHARE) Munich Germany Max Planck Institute for Social Law and Social Policy (MPISOC) Munich Germany Faculty of Medicine Mansoura University Mansoura Egypt

Background: Aging is frequently accompanied by multimorbidity, the presence of multiple chronic conditions, which contributes to declines in both cognitive and physical function and presents complex health challenges. One such challenge is Polypharmacy (PP), defined as the concurrent use of more than five medications. Methods: We used data from participants older than 50 years who were present in wave 6 and at least one of the subsequent three waves of the SHARE study, aiming to predict PP risk at 2, 4, and 6-year intervals. We selected the predictor variables using LASSO regression and evaluated eight ML models using a rigorous cross-validation strategy to ensure robustness and reliability. Findings: Our analysis reveals an upward trend in PP prevalence across the surveyed countries, with aggregate figures rising from 34.03% (95% CI 33.1-34.9) in wave 7 to 36.75% (95% CI 35.6-37.9) in wave 8, reaching 39.91% (95% CI 38.9-40.9) in wave 9. LASSO regression identified 17 key predictors of PP risk, which were related to socio-demographic factors, lifestyle factors, physical and mental health, and disease history. Among the models evaluated, the Categorical Boosting ML model performed best, yielding overall accuracies of 75.08%, 73.7%, and 71.65% and recall rates of 72.83%, 70.48%, and 67.96% for the 2, 4, and 6-year intervals, respectively. Interpretation: This study uncovers a rising trend of PP. It demonstrated the potential of using longitudinal data and ML to predict PP. Moreover, our findings suggest that mental health is an important factor to consider when addressing PP. © 2025 International Brain Research Organization (IBRO)

关键词： Aging Longitudinal Machine Learning Polypharmacy Predictive SHARE

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Conditional diffusion model with spatial attention and latent embedding for medical image segmentation

arXiv

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arXiv 2025年

作者： Hejrati, Behzad Banerjee, Soumyanil Glide-Hurst, Carri Dong, Ming Department of Computer Science Wayne State University MI United States Department of Human Oncology University of Wisconsin-Madison WI United States

Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://***/Hejrati/cDAL/. © 2025, CC BY.

关键词： Image segmentation

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Virtual Visits, Real Emotions: Designing Social VR Experiences for Imprisoned Fathers and their Children 25

Virtual Visits, Real Emotions: Designing Social VR Experienc...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Linda Graf Leslie Scholemann Philipp Sykownik Johannes Fuss Maic Masuch Faculty of Computer Science / Department of Human-centered Computing and Cognitive Science (HCCS) / Entertainment Computing Group/ University of Duisburg-Essen Duisburg Germany Faculty of Computer Science / Department of Human-centered Computing and Cognitive Science (HCCS) / Entertainment Computing Group/ Duisburg Germany Institute of Forensic Psychiatry and Sex Research University of Duisburg-Essen Essen Germany

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GMOCSO: Multi-objective Cat Swarm Optimization Algorithm based on a Grid System

arXiv

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arXiv 2025年

作者： Ahmed, Aram M. Hassan, Bryar A. Rashid, Tarik A. Noori, Kaniaw A. Saeed, Soran Ab.M. Ahmed, Omed H. Umar, Shahla U. Computer Science and Engineering Department University of Kurdistan Hewlêr Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Information Technology University of Human Development Sulaymaniyah Iraq Network Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq

This paper presents a multi-objective version of the Cat Swarm Optimization Algorithm called the Grid-based Multiobjective Cat Swarm Optimization Algorithm (GMOCSO). Convergence and diversity preservation are the two main goals pursued by modern multi-objective algorithms to yield robust results. To achieve these goals, we first replace the roulette wheel method of the original CSO algorithm with a greedy method. Then, two key concepts from Pareto Archived Evolution Strategy Algorithm (PAES) are adopted: the grid system and double archive strategy. Several test functions and a real-world scenario called the Pressure vessel design problem are used to evaluate the proposed algorithm's performance. In the experiment, the proposed algorithm is compared with other well-known algorithms using different metrics such as Reversed Generational Distance, Spacing metric, and Spread metric. The optimization results show the robustness of the proposed algorithm, and the results are further confirmed using statistical methods and graphs. Finally, conclusions and future directions were presented.. © 2025, CC BY.

关键词： Pressure vessels

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Secure Resource Management in Cloud Computing: Challenges, Strategies and Meta-Analysis

arXiv

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arXiv 2025年

作者： Saxena, Deepika Swain, Smruti Rekha Kumar, Jatinder Patni, Sakshi Gupta, Kishu Singh, Ashutosh Kumar Lindenstruth, Volker Division of Information Systems University of Aizu Aizuwakamatsu9650006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications National Institute of Technology Kurukshetra Kurukshetra136119 India Department of Computing Gachon University Seongnam461701 Korea Republic of Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India Department of Computer Science and Engineering Goethe University Frankfurt60438 Germany

Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, the paper discusses the trade-offs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and secure resource management. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management. © 2025, CC BY-SA.

关键词： Cloud security

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Bridging Borders, Breaking Biases: Envisioning Technologies to Support North Korean Defectors in South Korea 25

Bridging Borders, Breaking Biases: Envisioning Technologies ...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Hayoun Noh Hyunah Jo Ge Wang Max Van Kleek Younah Kang Department of Computer Science University of Oxford Oxford United Kingdom Yonsei University Seoul Republic of Korea Institute for Human-Centered Artificial Intelligence (HAI) Stanford University Stanford California USA Information and Interaction Design Yonsei University Seoul Republic of Korea

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Raise Your Eyebrows Higher: Facilitating Emotional Communication in Social Virtual Reality Through Region-Specific Facial Expression Exaggeration 25

Raise Your Eyebrows Higher: Facilitating Emotional Communica...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Xueyang Wang Sheng Zhao Yihe Wang Howard Ziyu Han Xinge Liu Xin Yi Xin Tong Hewu Li Institute for Network Sciences and Cyberspace Tsinghua University Beijing China and Zhongguancun Laboratory Beijing China Weiyang College Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University pittsburgh Pennsylvania USA Department of Computer Science and Technology Tsinghua University Beijing China and Beijing National Research Center for Information Science and Technology Beijing China Thrust of Computational Media and Arts Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

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