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arXiv 2025年

作者： Elharrouss, Omar Akbari, Younes Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Engineering Qatar University Doha Qatar Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates

Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively. © 2025, CC BY.

关键词： Convolution

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Exploring Classification Models for Video Source Device Identification: A Study of CNN-SVM and Softmax Classifier

Exploring Classification Models for Video Source Device Iden...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Ottakath, Najmath Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Khelifi, Fouad Qatar University Department of Computer Science and Engineering Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates Northumbria University Department of Computer and Information Sciences Newcastle United Kingdom

ISBN: (纸本)9798350335590

Video Source device identification plays a crucial role in video forensics as the proliferation of video capturing devices has given rise to crimes with videos that are challenging to trace. Reliance on metadata extraction is insufficient as it can be corrupted or manipulated to conceal the source of the crime. Another technique employed for source identification is noise pattern extraction, which generates a unique identification for the video camera. However, this method is susceptible to capture faults and can produce diverse noise patterns for each video. In addressing these challenges, there is a need to identify distinctive features that are consistent across all videos captured by the same camera. This has led to the adoption of computer vision techniques utilizing machine learning and deep learning. Classifiers play a crucial role in machine learning and data analysis, as they are responsible for categorizing or predicting results based on input data. Our experiments show that the subject is sensitive to classifiers and developing a good classifier or classifier-level fusions can improve results in practice for all datasets. © 2023 IEEE.

关键词： Image classification

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Exploring Classification Models for Video Source Device Identification: A Study of CNN-SVM and Softmax Classifier

Exploring Classification Models for Video Source Device Iden...

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Najmath Ottakath Younes Akbari Somaya Al-Maadeed Ahmed Bouridane Fouad Khelifi Department of Computer Science and Engineering Qatar University Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah UAE Department of Computer and Information Sciences Northumbria University Newcastle UK

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On How data are Used in Model Development: The Elephant in the Room

SSRN

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SSRN 2023年

作者： Maier, Holger R. Zheng, Feifei Gupta, Hoshin Chen, Junyi Mai, Juliane Savic, Dragan Loritz, Ralf Wu, Wenyan Guo, Danlu Bennett, Andrew Zhao, Jianshi School of Architecture and Civil Engineering The University of Adelaide Adelaide Australia College of Civil Engineering and Architecture Zhejiang University China Department of Hydrology and Atmospheric Sciences The University of Arizona Tucson United States Computational Hydrosystems Helmholtz Centre for Environmental Research–UFZ Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence–ScaDS.AI Leipzig Germany KWR Water Research Institute Netherlands Centre for Water Systems University of Exeter Exeter United Kingdom Institute of Water and River Basin Management Karlsruhe Germany Department of Infrastructure Engineering The University of Melbourne Melbourne Australia School of Engineering College of Engineering Computing & Cybernetics Australian National University Canberra Australia Department of Hydraulic Engineering Tsinghua University Beijing China

Models are widely used to support the development of understanding regarding the physical nature of Earth and environmental systems, with a view to improving their planning and management. data is crucial to the development of such models, and it is common to partition the available data into separate subsets to be used for model development and evaluation. Although the manner by which the data is partitioned can significantly affect the model’s ability to generalize to novel conditions, this issue has (surprisingly) been largely ignored (i.e., the elephant in the room). Here we highlight the importance of careful attention to this aspect of the model development process and how it can impact the generalization ability of models. We report on the problems with commonly used, as well as more advanced data-splitting approaches. We also map out a way forward to enable us to address the elephant in the room. © 2023, The Authors. All rights reserved.

关键词： data handling

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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