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Innovative Quantum PlasmoVision-Based Imaging for real-time deepfake Detection

PLASMONICS 2025年 1-17页

作者： Maheshwari, R. Uma Jayasudha, A. R. Pandey, Binay Kumar Pandey, Digvijay Hindusthan Inst Technol Dept Elect & Commun Engn Coimbatore India Hindusthan Coll Engn & Technol Dept Comp Applicat Coimbatore India Govind Ballabh Pant Univ Agr & Technol Pantnagar Coll Technol Dept Informat Technol Pantnagar Udham Singh Nagar Uttarakhand India Dept Tech Educ Uttar Pradesh Kanpur India

In recent years, the proliferation of deepfake images has posed a substantial threat to media credibility, security, and privacy. Contemporary detection techniques, predominantly reliant on deep learning algorithms, fail to identify the nuanced pixel-level discrepancies inherent in deepfake material. This study introduces PlasmoVision, an innovative quantum-enhanced plasmonic imaging technology that incorporates AI-driven deep learning for highly sensitive real-time deepfake detection. deepfakes alter digital images and videos to produce very persuasive fraudulent content, rendering traditional detection methods ineffective. Plasmonic surface resonance technology, in conjunction with quantum dots, has the capacity to capture intricate image features that can disclose such alterations. Integrating deep learning into this detection system improves the accuracy and velocity of analysis. The PlasmoVision technology employs quantum dot-enhanced plasmonic arrays to detect sub-pixel-level resonance shifts resulting from light interaction with the image surface. The optical signals are analyzed with a sophisticated convolutional neural network (CNN) that categorizes images according to the plasmonic resonance data. The AI model is trained on a varied dataset of genuine and deepfake photos, attaining an ideal equilibrium between detection sensitivity and speed. real-time picture analysis is accomplished by swift plasmonic scanning and AI-driven classification. The suggested device attained an accuracy rate of 98.6% in identifying deepfakes within a test dataset, exhibiting a false positive rate of 1.2% and a false negative rate of 0.5%. The quantum-enhanced plasmonic system identified pixel abnormalities with a sensitivity of up to 10 nm, markedly surpassing conventional deepfake detection technologies. PlasmoVision real-time analysis capacity decreased processing time by 35% relative to traditional approaches, rendering it exceptionally appropriate for extensive and real-ti

关键词： Quantum dots Plasmonic surface resonance deepfake detection AI-driven deep learning Convolutional neural networks image authentication real-time analysis Sub-pixel detection Media security Optical sensing

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deep learning and Machine learning for Malaria Detection: Overview, Challenges and Future Directions

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第5期23卷 1745-1776页

作者： Jdey, Imen Hcini, Hazala Ltifi, Hela Sidi Bouzid Univ Kairouan Fac Sci & Technol Kairouan Tunisia Univ Sfax Natl Engn Sch Sfax ENIS ReGIM Lab Res Grp Intelligent Machines LR11ES48 Sfax Tunisia

Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints.

关键词： Malaria diagnosis machine learning deep learning convolutional neural network hybrid algorithms

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A lightweight deep learning method for real-time weld feature extraction under strong noise

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SIGNAL image AND VIDEO processing 2024年第11期18卷 8169-8184页

作者： Cheng, Jiaming Jin, Hui Southeast Univ Sch Civil Engn Jiangsu Key Lab Mech Anal Infrastructure & Adv Equ Nanjing Peoples R China

This paper proposes a lightweight deep learning (DL) framework for real-time accurate weld feature extraction from noisy images with light, smoke, or splash. Leveraging a two-dimensional human pose estimation paradigm, the framework follows a top-down architecture for accurate weld feature point localization. This study develops a semi-automatic annotation technique to dramatically reduce the annotation cost. Then, we design a lightweight yet faster You Only Look Once version 8 (YOLOv8) detector to rapidly detect the weld feature region in the presence of strong noise. To avoid reliance on high-resolution feature maps and achieve sub-pixel-level localization accuracy, a heatmap-free approach decomposes the feature point detection task into subtasks of horizontal and vertical coordinate classification. Comparison with mainstream DL-based weld recognition methods validates the superiority of the proposed method regarding real-time feature extraction accuracy and robustness.

关键词： Laser vision Pose estimation Weld feature extraction Coordinate classification YOLO

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Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

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DIGITAL SIGNAL processing 2024年 144卷

作者： Manisali, Irfan Oral, Okyanus Oktem, Figen S. Middle East Tech Univ METU Dept Elect & Elect Engn TR-06800 Ankara Turkiye

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics based learned reconstruction approach compared to commonly used ap

关键词： Radar imaging Near-field microwave imaging deep learning 3D inverse imaging problems Sparse MIMO array

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Enhancing fitness action recognition with ResNet-TransFit: Integrating IoT and deep learning techniques for real-time monitoring

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ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 89-101页

作者： Li, Jijie Gong, Ruyao Wang, Gang Kunsan Natl Univ Phys Educ Gunsan 54150 Jeollabuk South Korea Linyi Univ Coll Sports & Hlth Linyi 276000 Shandong Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo 315100 Peoples R China Imperial Coll London Dept Bioengn London SW7 2AZ England

With the growing popularity of fitness, the demand for real-time action recognition and feedback is increasing. Current research faces challenges in handling complex actions, real-time processing, and system integration. address these issues, we propose a novel fitness action recognition model that integrates ResNet, Transformer, and transfer learning techniques. Specifically, ResNet is used for image feature extraction, Transformer handles time-series data processing, and transfer learning accelerates the model's adaptation to new data. We evaluated our model on the NTU RGB+D action recognition dataset, achieving 48.5 ms latency, 29.1 fps throughput, and 93.7% accuracy, significantly outperforming other models. Our model achieved an accuracy improvement 5% over existing methods, demonstrating significant potential for real-time fitness monitoring. By incorporating IoT technology, our system enables real-time data processing and action recognition, making it ideal for smart fitness monitoring. Although the model has high complexity and memory usage, its efficiency and accuracy demonstrate its potential for widespread adoption. Future work will focus on optimizing the model structure and training methods to enhance applicability in resource-constrained environments, ensuring broader usability and efficiency in various real-world applications.

关键词： Action recognition image analysis ResNet Transformer Transfer learning

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Enhanced synthetic aperture radar image autofocus and classification using 2D SARNet framework

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JOURNAL OF APPLIED REMOTE SENSING 2024年第2期18卷

作者： Sakr, Mohamed Saleh, Ahmed AbdElkader, Fathy Amer, Ghada AboElenean, Mohamed Mil Tech Coll Elect Engn Cairo Egypt October 6 Univ Fac Informat & Comp Sci Giza Egypt MUST Univ Fac Engn Dept Elect Engn Giza Egypt

A synthetic aperture radar (SAR) system is a notable source of information, recognized for its capability to operate day and night and in all weather conditions, making it essential for various applications. SAR image formation is a pivotal step in radar imaging, essential for transforming complex raw radar data into interpretable and utilizable imagery. Nowadays, advancements in SAR sensor design, resulting in very wide swaths, generate a massive volume of data, necessitating extensive processing. Traditional methods of SAR image formation often involve resource-intensive and time-consuming postprocessing. There is a vital need to automate this process in near-real-time, enabling fast responses for various applications, including image classification and object detection. We present an SAR processing pipeline comprising a complex 2D autofocus SARNet, followed by a CNN-based classification model. The complex 2D autofocus SARNet is employed for image formation, utilizing an encoder-decoder architecture, such as U-Net and a modified version of ResU-Net. Meanwhile, the image classification task is accomplished using a CNN-based classification model. This framework allows us to obtain near real-time results, specifically for quick image viewing and scene classification. Several experiments were conducted using real-SAR raw data collected by the European remote sensing satellite to validate the proposed pipeline. The performance evaluation of the processing pipeline is conducted through visual assessment as well as quantitative assessment using standard metrics, such as the structural similarity index and the peak-signal-to-noise ratio. The experimental results demonstrate the processing pipeline's robustness, efficiency, reliability, and responsivity in providing an integrated neural network-based SAR processing pipeline.

关键词： synthetic aperture radar (SAR) deep learning CNN-based model ResU-Net SAR autofocus

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A Regret Bound for the AdaMax Algorithm With image Segmentation Application

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2025年第9期48卷 10208-10214页

作者： Jirakipuwapat, Wachirapong King Mongkuts Univ Technol North Bangkok KMUTNB Fac Sci Energy & Environm Rayong Thailand

The AdaMax algorithm provides enhanced convergence properties for stochastic optimization problems. In this paper, we present a regret bound for the AdaMax algorithm, offering a tighter and more refined analysis compared to existing bounds. This theoretical advancement provides deeper insights into the optimization landscape of machine learning algorithms. Specifically, the You Only Look Once (YOLO) framework has become well-known as an extremely effective object segmentation tool, mostly because of its extraordinary accuracy in real-time processing, which makes it a preferred option for many computer vision applications. Finally, we used this algorithm for image segmentation.

关键词： AdaMax deep learning image segmentation regret bound YOLO

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A deep-learning Empowered, real-time processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1220-1230页

作者： Xia, Yunjia Chen, Jianan Li, Jinchen Gong, Tingchen Vidal-Rosas, Ernesto E. Loureiro, Rui Cooper, Robert J. Zhao, Hubin Univ Coll London UCL Div Surg & Intervent Sci HUB Intelligent Neuroengn HUBIN CREATe London WC1E 6BT England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England UCL Dept Med Phys & Biomed Engn DOT HUB London WC1E 6BT England

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

关键词： real-time systems US Department of Transportation Hemodynamics Functional near-infrared spectroscopy Optical imaging Three-dimensional displays Feedback amplifiers Jacobian matrices image reconstruction Electroencephalography Functional near-infrared spectroscopy (fNIRS) diffuse optical tomography (DOT) brain-computer interface (BCI) neurofeedback (NFB) motion artifacts deep learning real-time processing

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SDD-Net: self-supervised dual-domain dual-path single-pixel imaging

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OPTICS EXPRESS 2024年第17期32卷 30787-30799页

作者： Guo, Zhengmin Zhou, Pei Zhu, Jiangping Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis Chengdu 610065 Peoples R China

Existing supervised deep-learning single-pixel imaging methods mostly require paired label data to pre-train the network. Such training methods consume a considerable amount of time to annotate the dataset and train the network. Additionally, the generalization ability of the network model limits the practical application of deep learning single-pixel imaging. Especially for complex scenes or specific applications, precise imaging details pose challenges to existing single-pixel imaging methods. To address this, this paper proposes a self-supervised dual-domain training only requires measuring the light intensity signal values and projection pattern images, without the need for actual labels to reconstruct the target image. The dual-domain constraint between the measurement domain and the image domain can better guide the uniqueness of image reconstruction. The structure-texture dual-path guides the network to recover the specificity of image structure information and texture information. Experimental results demonstrate that this method can not only reconstruct detailed information of complex images but also reconstruct high-fidelity images from low sampling rate measurements. Compared with the current state-of-the-art traditional and deep learning methods, this method exhibits excellent performance in both imaging quality and efficiency. When the sampling rate is 5.45%, the PSNR and SSIM indicators are improved by 5.3dB and 0.23, respectively. The promotion of this technology will contribute to the application of single-pixel imaging in military and real-time imaging fields.

关键词： deep learning Ghost imaging Imaging techniques real time imaging Single pixel imaging Spatial light modulators

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Detection of weeds in vegetables using image classification neural networks and image processing

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FRONTIERS IN PHYSICS 2025年 13卷

作者： Jin, Huiping Han, Kang Xia, Hongting Xu, Bo Jin, Xiaojun Nanjing Forestry Univ Engn Training Ctr Nanjing Jiangsu Peoples R China Peking Univ Shandong Lab Adv Agr Sci Weifang Inst Adv Agr Sci Weifang Shandong Peoples R China Jurong Inst Smart Agr Zhenjiang Jiangsu Peoples R China

Weed management presents a major challenge to vegetable growth. Accurate identification of weeds is essential for automated weeding. However, the wide variety of weed types and their complex distribution creates difficulties in rapid and accurate weed detection. In this study, instead of directly applying deep learning to identify weeds, we first created grid cells on the input images. image classification neural networks were utilized to identify the grid cells containing vegetables and exclude them from further analysis. Finally, image processing technology was employed to segment the non-vegetable grid images based on their color features. The background grid cells, which contained no green pixels, were identified, while the remaining cells were labeled as weed cells. EfficientNet, GoogLeNet, and ResNet models achieved overall accuracies of over 0.956 in identifying vegetables in the testing dataset, demonstrating exceptional identification performance. Among these models, the ResNet model exhibited the highest computational efficiency, with a classification time of 12.76 ms per image and a corresponding frame rate of 80.31 fps, satisfying the requirement for real-time weed detection. Effectively identifying vegetables and differentiating weeds from soil significantly reduces the complexity of weed detection and improves its accuracy.

关键词： weed detection deep learning image classification neural networks image processing weed management

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