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arXiv 2025年

作者： Shao, Shuo Zhu, Haozhe Yao, Hongwei Li, Yiming Zhang, Tianwei Qin, Zhan Ren, Kui State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Department of Computer Science City University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models by preventing their unauthorized reuse. It is promising and convenient since it does not necessitate modifying the protected model. In this paper, we revisit existing fingerprinting methods and reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model. We demonstrate that this vulnerability mostly stems from their untargeted nature, where they generally compare the outputs of given samples on different models instead of the similarities to specific references. Motivated by these findings, we propose a targeted fingerprinting paradigm (i.e., FIT-Print) to counteract false claim attacks. Specifically, FIT-Print transforms the fingerprint into a targeted signature via optimization. Building on the principles of FIT-Print, we develop bit-wise and list-wise black-box model fingerprinting methods, i.e., FIT-ModelDiff and FIT-LIME, which exploit the distance between model outputs and the feature attribution of specific samples as the fingerprint, respectively. Extensive experiments on benchmark models and datasets verify the effectiveness, conferrability, and resistance to false claim attacks of our FIT-Print. © 2025, CC BY.

关键词： Open systems

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MGSR: 2D/3D MUTUAL-BOOSTED GAUSSIAN SPLATTING FOR HIGH-FIDELITY SURFACE RECONSTRUCTION UNDER VARIOUS LIGHT CONDITIONS

arXiv

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arXiv 2025年

作者： Zhou, Qingyuan Gong, Yuehu Yang, Weidong Li, Jiaze Luo, Yeqi Xu, Baixin Li, Shuhao Fei, Ben He, Ying School of Computer Science Fudan University China School of Data Science Fudan University China College of Computing and Data Science Nanyang Technological University Singapore Department of Information Engineering The Chinese University of Hong Kong Hong Kong

Novel view synthesis (NVS) and surface reconstruction (SR) are essential tasks in 3D Gaussian Splatting (3D-GS). Despite recent progress, these tasks are often addressed independently, with GS-based rendering methods struggling under diverse light conditions and failing to produce accurate surfaces, while GS-based reconstruction methods frequently compromise rendering quality. This raises a central question: must rendering and reconstruction always involve a trade-off? To address this, we propose MGSR, a 2D/3D Mutual-boosted Gaussian splatting for Surface Reconstruction that enhances both rendering quality and 3D reconstruction accuracy. MGSR introduces two branches—one based on 2D-GS and the other on 3D-GS. The 2D-GS branch excels in surface reconstruction, providing precise geometry information to the 3D-GS branch. Leveraging this geometry, the 3D-GS branch employs a geometry-guided illumination decomposition module that captures reflected and transmitted components, enabling realistic rendering under varied light conditions. Using the transmitted component as supervision, the 2D-GS branch also achieves high-fidelity surface reconstruction. Throughout the optimization process, the 2D-GS and 3D-GS branches undergo alternating optimization, providing mutual supervision. Prior to this, each branch completes an independent warm-up phase, with an early stopping strategy implemented to reduce computational costs. We evaluate MGSR on a diverse set of synthetic and real-world datasets, at both object and scene levels, demonstrating strong performance in rendering and surface reconstruction. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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Digital Forensics Analysis of iMazing Phone Explorer Tool to Aid Mobile Forensics Investigation

Digital Forensics Analysis of iMazing Phone Explorer Tool to...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Ashar Neyaz Avinash Kumar Bingyu Liu School of Computing & Data Science Wentworth Institute of Technology Boston MA USA Department of Computer Science Sam Houston State University Huntsville TX USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Apple devices continue to lead the smart device industry, with over 2.2 billion active devices worldwide. These devices, including iPhones and iPads, store extensive personally identifiable information (PII), making them vital in digital forensic investigations. This study specifically examines the retrieval and analysis of PII from an iPhone and an iPad using iMazing software as the sole forensic tool. data acquisition was conducted via a Lightning-to-VSB-A connection without jailbreaking, focusing on two distinct scenarios-one for each device-restricting access to user-space data. The analysis uncovered a range of forensic artifacts, including iCloud-synced SSID data, message logs, Wi-Fi security protocols, and other critical digital traces. Extracted data appeared in multiple formats, such as text files, CSV files, and plist files, providing forensic investigators with valuable insights. While this study is limited to iMazing, its findings highlight the potential and limitations of third-party tools in mobile forensics. By leveraging iMazing's capabilities, this research contributes to the evolving field of mobile device forensics, reinforcing the role of Apple devices in enhancing digital investigative methodologies.

关键词： data privacy Protocols Digital forensics Tablet computers Software Mobile handsets Security Smart devices Wireless fidelity Identification of persons

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One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing

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IEEE Transactions on Multimedia 2025年

作者： Ji, Yuzhu Zheng, Chuanxia Cham, Tat-Jen Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China University of Oxford Department of Engineering Science Oxford United Kingdom Nanyang Technological University College of Computing and Data Science Singapore Singapore

Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between source and driving poses due to the large variation in motion and articulation complexity. In addition, the accuracy and precision of DensePose degrade the image quality for neural-rendering-based methods. To address the limitations and by both considering the importance of appearance and geometry for motion transfer, in this work, we proposed a unified framework that combines multi-scale feature warping and neural texture mapping to recover better 2D appearance and 2.5D geometry, partly by exploiting the information from DensePose, yet adapting to its inherent limited accuracy. Our model takes advantage of multiple modalities by jointly training and fusing them, which allows it to robust neural texture features that cope with geometric errors as well as multi-scale dense motion flow that better preserves appearance. Experimental results with full and half-view body video datasets demonstrate that our model can generalize well and achieve competitive results, and that it is particularly effective in handling challenging cases such as those with substantial self-occlusions. © 1999-2012 IEEE.

关键词： Mapping

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Domain Generalization Study of Empirical Risk Minimization from Causal Perspectives

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IEEE Transactions on Multimedia 2025年

作者： Mo, Zhenling Zhang, Zijun Tsui, Kwok-Leung City University of Hong Kong Department of Data Science College of Computing Kowloon Hong Kong Virginia Tech Grado Department of Industrial and Systems Engineering BlacksburgVA United States

Empirical risk minimization (ERM) is a celebrated induction principle for developing data-driven models. However, ERM has received both pros and cons for its capability on domain generalization (DG). To this end, this paper attempts to study the success and failure of ERM at supervised DG classification tasks, both theoretically and empirically, with causal perspectives. In the theoretical aspect, we first explore different properties of a causal metric termed information flow, followed with discussing relationships between the information flow and the mutual information in the proposed causal graph. Next, we analyze the roles of the transformed causal feature and the transformed spurious feature on modeling performances. It reveals that the interaction between the spurious influencer and the transformed causal feature is the key determining the failure or success of ERM on DG. In the empirical study, we first simulate various DG settings based on the MNIST, Fashion MNIST, and CIFAR10 datasets. Next, we verify developed theories by testing three different neural network configurations in designed experiments. In addition, experiments based on real-world datasets are conducted to further consolidate key points of the proposed theories. To extend application benefits of the theoretical discoveries, a new risk minimization framework with a novel feature intervention for regulating ERM is proposed. It achieves DG improvements over ERM on real-world datasets of image segmentation, image classification, and text classification. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Exploring Multimodal Generative AI for Education through Co-design Workshops with Students 25

Exploring Multimodal Generative AI for Education through Co-...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Prajish Prasad Rishabh Balse Dhwani Balchandani School of Computing and Data Sciences FLAME University Pune India Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

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Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G

arXiv

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arXiv 2025年

作者： Liu, Zhiyan Chen, Xu Wu, Hai Wang, Zhanwei Chen, Xianhao Niyato, Dusit Huang, Kaibin Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Sensing and edge artificial intelligence (AI) are envisioned as two essential and interconnected functions in sixth-generation (6G) mobile networks. On the one hand, sensing-empowered applications rely on powerful AI models to extract features and understand semantics from ubiquitous wireless sensors. On the other hand, the massive amount of sensory data serves as the fuel to continuously refine edge AI models. This deep integration of sensing and edge AI has given rise to a new task-oriented paradigm known as integrated sensing and edge AI (ISEA), which features a holistic design approach to communication, AI computation, and sensing for optimal sensing-task performance. In this article, we present a comprehensive survey for ISEA. We first provide technical preliminaries for sensing, edge AI, and new communication paradigms in ISEA. Then, we study several use cases of ISEA to demonstrate its practical relevance and introduce current standardization and industrial progress. Next, the design principles, metrics, tradeoffs, and architectures of ISEA are established, followed by a thorough overview of ISEA techniques, including digital air interface, over-the-air computation, and advanced signal processing. Its interplay with various 6G advancements, e.g., new physical-layer and networking techniques, are presented. Finally, we present future research opportunities in ISEA, including the integration of foundation models, convergence of ISEA and integrated sensing and communications (ISAC), and ultra-low-latency ISEA. Copyright © 2025, The Authors. All rights reserved.

关键词： Wireless sensor networks

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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Multimodal Fusion with Deep Neural Network Architecture for Alzheimer's Disease Prediction 15th

Multimodal Fusion with Deep Neural Network Architecture for ...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Venkatesh, Swarna Saritha, Kuppala Gogula, Sreenivasulu Chitteti, Chengamma Yalavarthi, Sikhi Swaraja, K. ECE CMR Technical Campus Hyderabad India CSE Presidency University Bangalore India Computer Science and Engineering. (Data Science) Vardhaman College of Engineering Hyderabad India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupati India Lexisnexis Risk Solutions Inc. Oklahomacity United States ECE GRIET Hyderabad India

ISBN: (纸本)9783031810824

Alzheimer's disease occurs due to neurodegenerative disorder, innovative approach is needed for early detection of disease. A method combining multimodal data fusion with adaptive capabilities of Adam optimization algorithm to enhance Alzheimer's disease prediction have been proposed. The proposed multimodal fusion approach combines neuroimaging, clinical, and genetic data into a deep neural network architecture, in order to achieve better accuracy,. Utilization of multimodal data helps to capture the view of disease, from structural brain changes to clinical assessments. This approach not only enhances predictive accuracy but also provides valuable insights into the mechanisms of Alzheimer's disease prediction. Utilization of Adam's optimization algorithm helps the model to converge faster making the proposed method highly effective. Combining the strengths of multimodal fusion and Adam optimization, the proposed method can be utilized for early Alzheimer's disease diagnosis, leading to improved patient outcomes and improved accuracy in Alzheimer's disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN Deep Neural Network Dense Layers Multimodal Fusion RNN

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Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges

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Computers in Biology and Medicine 2025年 189卷 109834-109834页

作者： Ibrahim, Mahmoud Khalil, Yasmina Al Amirrajab, Sina Sun, Chang Breeuwer, Marcel Pluim, Josien Elen, Bart Ertaylan, Gökhan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands VITO Belgium Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our aim is to offer insights into their current and future applications in medical research, particularly in the context of synthesis applications, generation techniques, and evaluation methods, as well as providing a GitHub repository as a dynamic resource for ongoing collaboration and innovation. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered in previous reviews. The survey also emphasizes the aspect of conditional generation, which is not focused on in similar work. Key contributions include a broad, multi-modality scope that identifies cross-modality insights and opportunities unavailable in single-modality surveys. While core generative techniques are transferable, we find that synthesis methods often lack sufficient integration of patient-specific context, clinical knowledge, and modality-specific requirements tailored to the unique characteristics of medical data. Conditional models leveraging textual conditioning and multimodal synthesis remain underexplored but offer promising directions for innovation. Our findings are structured around three themes: (1) Synthesis applications, highlighting clinically valid synthesis applications and significant gaps in using synthetic data beyond augmentation, such as for validation and evaluation;(2) Generation techniques, identifying gaps in personalization and cross-modality innovation;and (3) Evalu

关键词： Generative adversarial networks

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