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Advancing Copy-Move Manipulation detection in Complex Image Scenarios Through Multiscale Detector

IEEE ACCESS 2024年 12卷 64736-64753页

作者： Diwan, Anjali Mahadeva, Rajesh Gupta, Vinay Marwadi Univ Dept CE AI Rajkot 360003 Gujarat India Khalifa Univ Dept Phys Abu Dhabi U Arab Emirates

This research presents a new approach for identifying instances of copy-move forgeries in digital images by utilizing the Multiscale Detector a Neural Network-based method, which serves as an image key-point detector and descriptor. The act of copy-move manipulation involves the replication and subsequent insertion of a specific segment of an image, intending to modify the overall content of the image. The approach we utilize leverages the sophisticated functionalities of Multiscale Detector, a framework that combines key-point detection with descriptor extraction, to accurately detect and localize instances of copy-move manipulation. The effectiveness of our approach is assessed on a range of copy-move forgeries, encompassing instances that have undergone post-processing and geometric transformations. The experimental findings illustrate the resilience of our approach in identifying instances of manipulations over a diverse range of textured images and various alteration approaches. Furthermore, our approach demonstrates strong performance even when subjected to supplementary processing procedures such as brightness modification, color reduction, contrast adjustment, and blurring. Our suggested method has greater performance when compared to the existing manipulation detection approach, as demonstrated through a comparative analysis. In addition, the algorithm we have developed has high computing efficiency, allowing for real-time detection of forgeries. The methodology employed in this study, which builds upon the Multiscale Detector framework, offers a highly effective approach to the detection of copy-move manipulations in digital images.

关键词： Forgery Feature extraction Digital images Image coding Image quality Image color analysis Neural networks Computer vision detection algorithms Copy-move forgery multiscale detector neural network computer vision image forensics forgery detection

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Pilot spoofing detection based on pilot random block encryption

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WIRELESS NETWORKS 2024年第2期30卷 973-985页

作者： Wang, Shiguo Liu, Hongdong Ruby, Rukhsana Ruan, Xiukai Zhang, Yongjian Changsha Univ Sci & Technol Comp & Commun Engn Inst Changsha 410114 Peoples R China Shenzhen Univ Coll Comp Sci Shenzhen 518060 Peoples R China Wenzhou Univ Inst Intelligent Locks Wenzhou 325035 Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Int Relat Sch Informat Sci & Technol Beijing 100091 Peoples R China

For wireless communication systems with large-scale multiple-input-multiple-output array in the presence of an active eavesdropper, the eavesdropper masquerades as a legitimate user sending the identical pilot sequence to manipulate channel estimation in the uplink training phase, which misleads precoding at the transmitter. If such pilot spoofing attack is not handled effectively, it will not only reduce system capacity, but also cause much signal power leak to the spoofer, and so it seriously threatens the security transmission in wireless communication. Nevertheless, how to detect such potential pilot spoofing and recover the contaminated channel plays a significant role in designing combating schemes and ensuring security transmission. In this paper, the impact of pilot spoofing attack on the system security capacity is investigated, and it is shown that pilot spoofing attack will degrade system capacity greatly and the transmitted information will be intercepted by the spoofer. Then, a novel detection scheme based on pilot-random-block-encryption is proposed, through which not only the detection probability on the potential pilot spoofing can be improved greatly, but also the channel state information of the legal user and the spoofer are obtained. Simulation results show that the detection probability of the proposed detection scheme reaches 93% even the transmit power of pilot spoofing is as low as - 10 dB, which is much greater than other existing schemes, and the system security capacity is also enhanced with the proposed channel estimation strategy.

关键词： Communication system security Physical layer Channel estimation detection algorithms

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DCGC-YOLO: The Efficient Dual-Channel Bottleneck Structure YOLO detection Algorithm for Fire detection

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IEEE ACCESS 2024年 12卷 65254-65265页

作者： He, Yun Hu, Junjie Zeng, Ming Qian, Yingjing Zhang, Renmin Jishou Univ Sch Commun & Elect Engn Jishou 416000 Hunan Peoples R China Huaihua Univ Key Lab Intelligent Control Technol Wuling Mt Ecol Huaihua 418000 Hunan Peoples R China

Fast and highly accurate fire detection algorithms are crucial for production and daily life. However, detecting fires in the early stages is challenging due to the lack of distinct features and fixed shapes of flames and smoke. To address this issue, we propose a fire detection algorithm, DCGC(Dual Channel Group Convolution)-YOLO, based on an improved YOLOv5 model. The DCGC-YOLO model introduces a new layer structure and anchor algorithm to optimize original YOLOv5 model. Firstly, we introduce a Cross Stage Partial (CSP) structure with a cascade of large convolutional kernels in the bottleneck layer. This structure increases network's receptive field, enhances feature extraction capabilities, and employs a channel cleansing mechanism that combines various channels separated by group convolution, promoting information exchange in the channel dimension and enabling better information encoding. Next, we integrate the Effective Squeeze and Extraction (eSE) mechanism into the new layer structure, enhancing the model's ability for long-range modeling and focusing more on target areas. Finally, we utilize an Intersection over Union (IoU)-based anchor generation algorithm to adjust the anchor sizes on custom fire dataset, enhancing the model's robustness and improving detection accuracy. Experimental results on our custom fire dataset demonstrate that the proposed DCGC-YOLO algorithm effectively detects targets with mAP of 41.1%, which is 2.9% higher than YOLOv5s, while reducing network parameters and computational complexity. To further validate the effectiveness of our proposed algorithm, we conduct experiments on the COCO2017 dataset. The results show that DCGC-YOLO achieves mAP of 38.9%, demonstrating strong generalization and competitiveness compared to state-of-the-art detectors.

关键词： YOLO Feature extraction Fires detection algorithms Kernel Convolution Detectors Computer vision object detection fire detection smoke detection YOLOv5

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IoT-Enabled Early detection of Diabetes Diseases Using Deep Learning and Dimensionality Reduction Techniques

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IEEE ACCESS 2024年 12卷 143016-143028页

作者： Vaiyapuri, Thavavel Alharbi, Ghada Muttipoll Dharmarajlu, Santhi Bouteraa, Yassine Misra, Sanket Venkata Naga Ramesh, Janjhyam Nandan Mohanty, Sachi Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj 54112 Saudi Arabia Taibah Univ Coll Comp Sci & Engn Dept Artificial Intelligence & Data Sci Madinah 42353 Saudi Arabia Jazan Univ Coll Nursing & Hlth Sci Dept Nursing Jazan 45142 Saudi Arabia VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati 522241 Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 263136 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttaranchal India

Chronic diseases, such as diabetes, cause serious challenges worldwide due to their long-lasting effect on health and quality of life. Diabetes, considered a high glucose level, requires continuous management to avoid complications like kidney failure, low vision, and heart disease. Leveraging state-of-the-art technology, especially IoT devices, shows great potential for optimizing the recognition and management of diabetes. These devices, including smart glucose monitors and wearable sensors, provide real-time data on crucial health metrics, enabling early intervention and proactive monitoring. Furthermore, incorporating deep learning (DL) techniques improves data analysis and recognizes risk factors and subtle patterns related to diabetes. By integrating IoT technology with DL techniques, the healthcare system can empower patients with tools for self-management and develop more conventional approaches for earlier diagnosis and personalized treatment plans, ultimately improving long-term health outcomes and reducing the burden of diabetes. This study develops a new IoT-enabled Driven Early detection of Chronic Disease using the DL and Dimensionality Reduction (EDCD-DLDR) approach. The EDCD-DLDR method aims to enable IoT devices to collect patient medical data and employ the DL model for earlier diagnosis of diabetes. In the EDCD-DLDR technique, the IoT-based data acquisition process is initially involved, and the collected data gets normalized using Z-score normalization. The EDCD-DLDR technique uses an artificial rabbit optimizer-based feature selection (ARO-FS) approach for dimensionality reduction. In addition, the detection of diabetes is achieved by the attention bidirectional gated recurrent unit (ABiGRU) model. The pelican optimization algorithm (POA) based hyperparameter selection model is included to improve the performance of the ABiGRU network. A comprehensive set of simulations is made to highlight the performance of the EDCD-DLDR method on the Kaggle d

关键词： Diabetes Internet of Things Diseases Medical services Data models Feature extraction Convolutional neural networks Deep learning Machine learning detection algorithms chronic disease deep learning pelican optimization algorithm machine learning

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A Distributed Knowledge Distillation Framework for Financial Fraud detection Based on Transformer

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IEEE ACCESS 2024年 12卷 62899-62911页

作者： Tang, Yuxuan Liu, Zhanjun Southwestern Univ Finance & Econ Sch Accounting Chengdu 611130 Sichuan Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

Financial fraud cases causing serious damage to the interests of investors are not uncommon. As a result, a wide range of intelligent detection techniques are put forth to support financial institutions' decision-making. Currently, existing methods have problems such as poor detection accuracy, slow inference speed, and weak generalization ability. Therefore, we suggest a distributed knowledge distillation architecture for financial fraud detection based on Transformer. Firstly, the multi-attention mechanism is used to give weights to the features, followed by feed-forward neural networks to extract high-level features that include relevant information, and finally neural networks are used to categorize financial fraud. Secondly, for the problem of inconsistent financial data indicators and unbalanced data distribution focused on different industries, a distributed knowledge distillation algorithm is proposed. This algorithm combines the detection knowledge of the multi-teacher network and migrates the knowledge to the student network, which detects the financial data of different industries. The final experimental results show that the proposed method outperforms other methods in terms of F1 score (92.87%), accuracy (98.98%), precision (81.48%), recall (95.45%), and AUC score (96.73%) when compared to the traditional detection methods.

关键词： Fraud Data models Finance Transformers Task analysis Hidden Markov models Feature extraction detection algorithms Investment Knowledge transfer Information analysis Transformer knowledge distillation financial fraud detection

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Single-Frame Difference-Based Image Fusion Glare-Resistant detection System in Green Energy Vehicles

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IEEE ACCESS 2024年 12卷 110977-110991页

作者： Lyu, Xiang Wang, Nan Gao, Jia Xinyang Agr & Forestry Univ Sch Informat Engn Xinyang 464000 Peoples R China SEGi Univ Grad Sch Business GSB Kota Damansara 47810 Petaling Jaya Malaysia

Green energy vehicles often use technologies that reduce lower inherent noise. However, adverse weather condition and low visibility at night can cause a glare effect from the headlights of oncoming cars. This poses a major threat to traffic safety. In order to solve this problem, this study initially adopts single frame difference for video frame selection, which reduces the computational load of image processing pipeline. Then, combined with visible and infrared images, this paper uses non-downsampled contourlet transform to achieve glare elimination. Finally, an improved convolutional network is used to detect pedestrians in anti-glare images, and volumetric Kalman filter algorithm is used to track pedestrians. Through these operations, the research establishes a Single-Frame Difference-Based Image Fusion Glare-Resistant detection System applicable to green energy vehicles. The experimental analysis shows that the designed system can eliminate glare more than 80%, and the pedestrian detection accuracy reaches 95.44%. The constructed system aids green energy vehicles in accurately perceiving their surroundings during nighttime driving, ensuring safe travel.

关键词： Pedestrians Image processing Night vision Image fusion Accuracy Safety Image color analysis Green energy detection algorithms Green energy vehicles single-frame difference image fusion glare-free pedestrian detection

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MGDE-YOLO: An Improved Lightweight Algorithm for Personnel Departure detection Based on YOLOv7

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IEEE ACCESS 2024年 12卷 150592-150603页

作者： Xu, Haiqiang Xue, Renzheng Zhang, Zifeng Hua, Shijie Qiqihar Univ Sch Comp & Control Engn Qiqihar 161000 Peoples R China Qiqihar Univ Key Lab Big Data Network Secur Detect & Anal Qiqihar 161000 Heilongjiang Peoples R China

Given the high complexity of office environments and the dense occlusion of personnel, which lead to low accuracy and high model complexity in detecting departures, this paper proposes MGDE-YOLO-a lightweight algorithm for personnel departure detection based on an improved YOLOv7 model. First, MobileNetV3 replaces the backbone feature extraction network of YOLOv7, thereby reducing the model's parameter count. Next, the new SPPCSPC_Ghost module, built with GhostConv, replaces the original SPPCSPC module. This change lowers model parameters, and increases feature fusion speed, which improves detection accuracy. The ultra-lightweight dynamic upsampling operator, DySample, is also introduced to update the model's upsampling operations, thereby reducing computational costs and enhancing performance. Finally, we propose the F-ECA attention module, which allows the model to better focus on essential features while suppressing unimportant ones. This improvement reduces missed and incorrect detections. Experimental results show that the MGDE-YOLO model reduces the number of parameters and computational load by 40.5% and 63.4%, respectively, compared to the original YOLOv7 model. Additionally, we compress the model's weight size to 40.1% of the original, reduce inference time by 31.8%, and improve the mean average precision by 2.3%, reaching 97.6%. This method significantly enhances the accuracy and speed of personnel absence detection in complex backgrounds while preserving the model's lightweight nature and practicality, making it suitable for deployment in natural office environments.

关键词： Personnel Feature extraction Accuracy Computational modeling Load modeling YOLO detection algorithms Head Complexity theory Neck Departure detection YOLOv7 lightweight MobileNetV3 DySample attention mechanism

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SHIP-YOLO: A Lightweight Synthetic Aperture Radar Ship detection Model Based on YOLOv8n Algorithm

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IEEE ACCESS 2024年 12卷 37030-37041页

作者： Luo, Yonghang Li, Ming Wen, Guihao Tan, Yunfei Shi, Chaoshan Chongqing Normal Univ Coll Comp & Informat Sci Shapingba 401331 Peoples R China

In response to the challenges posed by small objects, high noise, and complex backgrounds in synthetic aperture radar (SAR) ship detection, we proposed a lightweight model called SHIP-YOLO. In the neck of YOLOv8n, we replaced ordinary convolution (Conv) with a lighter ghost convolution (GhostConv) and introduced reparameterized ghost (RepGhost) bottleneck structure in C2f module. We then introduced Wise-IoU (WIoU) into the algorithm to improve the localization ability of the detection box. Finally, shuffle attention (SA) modules were added to the backbone and neck of YOLOv8n to enhance the perception capability of the target area. The results confirm that, compared with YOLOv8n, the proposed SHIP-YOLO on SAR Ship detection dataset (SSDD) reduces the parameters and floating-point operations (FLOPs) by 17% and 11%, respectively, and improves the precision, recall, and mean average precision (mAP) (0.5) by 1.7%, 0.1%, and 0.2%, respectively. The proposed model also showed strong generalization ability on another Sar-Ship-Dataset.

关键词： Computer vision Deep learning Object detection Radar remote sensing Synthetic aperture radar Noise measurement YOLO Marine vehicles Location awareness Convolutional neural networks detection algorithms deep learning object detection radar remote sensing synthetic aperture radar

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YOLOv5-S2C2: An Improved Method of Mask detection Based on Lightweight

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IEEE ACCESS 2024年 12卷 53306-53319页

作者： Xie, Zongyuan Ma, Hongyan He, Wei Xu, Jiechuan Wen, Haoyu Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Natl Virtual Simulat Expt Ctr Smart City Educ Beijing 100044 Peoples R China

Masks play a crucial role in preventing respiratory diseases and have diverse applications in national public health and industrial safety. Efficient mask-wearing detection systems are essential for ensuring accuracy and real-time performance. To overcome the challenges of extensive model calculations, extensive parameter volume, and complex hardware deployment in the current mask-wearing detection system, a lightweight mask detection model with improved YOLOv5 is proposed. Firstly, this study proposes a new lightweight network-EMA-FasterNet, as the backbone network of YOLOv5, which reduces the computation while preserving the information of each channel. Secondly, using Depthwise Separable Convolutions (DepthSepConv) to replace some C3 modules in the Neck further compresses the model volume, parameters, and computation. Finally, to prevent the detection of missing objects due to the removal of overlapping candidate boxes, use Soft Non-Maximum Suppression (NMS) to replace NMS. Compared with YOLOv5s, the proposed YOLOv5-S2C2 has high mAP in both Dataset I and Dataset II, while the parameters and computation of the model are reduced by about 56% and 57.0%, respectively. The volume is about 44% of the original model and the inference speed on GPU is improved by about 30%. These improvements show that the proposed model achieves a lightweight design and excellent real-time performance. It is also well suited for efficient use on hardware with limited computational resources.

关键词： YOLO Computational modeling Feature extraction Neck Solid modeling Real-time systems Classification algorithms Image processing Deep learning detection algorithms deep learning detection algorithms YOLOv5 FasterNet lightweight network

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PEDNet: A Proposal Enhancement Dynamic Network for Fine-Grained Ship detection in Optical Remote Sensing Images

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IEEE ACCESS 2024年 12卷 129813-129825页

作者： Zhu, Shengbo Wei, Lisheng Anhui Polytech Univ Sch Elect Engn Wuhu 241000 Anhui Peoples R China

Fine-grained ship detection (FGSD) refers to identify and localize different ships in optical remote sensing images, which is utilized in both civilian and military areas widely. Nevertheless, the region proposal as an essential component of ship detection and recognition, is restricted to design a multitask learning of FGSD, from proposal generation, representation, and utilization. In this paper, we propose a proposal enhancement dynamic network (PEDNet), to deal with the subtasks of FGSD better. First, the lateral scale-equalizing feature pyramid network (LSE-FPN) is designed to deliver equalized scale features for the subsequent generation of more precise proposals. Second, the multi-channel fusion network (MCFN) is proposed to extract independent and discriminative feature representations in proposals. In addition, we present an oriented dynamic prediction network (ODPN), which employs the dynamic training strategy to provide guidance for model training, to leverage high-quality proposals effectively for ship identification and localization. Finally, PEDNet is trained and validated on ShipRSImageNet, FGSD2021, DOSR, and MAR20 datasets. Quantitative and qualitative experimental results reveal that, compared with cutting-edge detectors, AP(50) of the PEDNet received by 74.58, 92.50, 61.28, and 80.84, AP(75) received by 68.34, 86.26, 46.19, and 69.18, AP (50:95) received by 56.25, 68.78, 40.17, and 55.54 on four datasets, respectively. It effectively proves that our method achieves advanced FGSD effectiveness and favorable generalization performance.

关键词： Proposals Feature extraction Marine vehicles Convolutional neural networks Detectors Remote sensing Accuracy Deep learning detection algorithms Computer vision object detection fine-grained ship detection (FGSD) deep learning remote sensing images

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