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Learnable Cross-Scale Sparse Attention Guided Feature Fusion for UAV Object detection

IEEE ACCESS 2024年 12卷 114212-114226页

作者： Zuo, Xin Qi, Chenhui Chen, Yifei Shen, Jifeng Fan, Heng Yang, Wankou Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212003 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Univ North Texas Dept Comp Sci & Engn Denton TX 76207 USA Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China

Object detection in Unmanned Aerial Vehicle (UAVs) faces a significant challenge in computer vision. Traditional methods are difficult to model object appearance feature with large scale variations and viewpoint differences, when drones fly at different altitudes and capture images from diverse shooting angles. To address this issue, we propose a Learnable Cross-scale Sparse Attention (LCSA) guided feature fusion method to improve the performance of UAV object detection. Specifically, the LCSA feature fusion module enables each point in a feature map to aggregate discriminative information from a set of points with learnable offsets in neighbor feature maps. It enhances local discriminative features of the object by facilitating semantic information interaction across multiple feature maps. The LCSA can function as a novel neck method that complements the existing neck methods and is also transplantable to different object detection frameworks. Moreover, we also employ a scale-aware loss function to integrate the normalized Wasserstein distance with CIoU in order to improve the incompatibility of IoU for objects with large scale variance. Experimental results on the SeaDroneSeev2 and VisDrone2019-DET datasets show that the proposed method achieves superior performance. At a resolution of 640*640, our method achieves 81.9% AP50 and 47.4% AP on SeaDroneSeev2, surpassing baseline 4.9% and 4.8%, achieves state-of-the-art performance. Furthermore, our method outperforms baseline by 5% AP on VisDrone2019-DET. Code will be available at https://***/qch777/LSACF.

关键词： Feature extraction Object detection Autonomous aerial vehicles Attention mechanisms Aggregates Computer vision detection algorithms UAV object detection cross-scale feature fusion sparse attention

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Robust Color Image Hashing With Nonnegative Matrix Factorization and Saliency Map for Copy detection

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IEEE MULTIMEDIA 2024年第3期31卷 44-52页

作者： Zhang, Hanyun Tang, Zhenjun Yu, Mengzhu Liang, Xiaoping Zhang, Xianquan Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541004 Peoples R China

Image hashing is an effective technique for copy detection. This article proposes a robust color image hashing scheme with nonnegative matrix factorization (NMF) and saliency maps. The proposed hashing scheme constructs a weighted feature map via the saliency map and the matrix of color vector angles. It extracts hash from the weighted feature map by using 2-D discrete wavelet transform (DWT), NMF, and ordinal measurements. The use of 2-D DWT achieves an initial data reduction, the NMF provides a parts-based representation, and the ordinal measurements can make a short and robust sequence. These techniques jointly contribute to a robust, short, and discriminative hash. Experiments of classification and copy detection are both done. Comparative results indicate that the proposed hashing scheme outperforms the compared hashing schemes.

关键词： Feature extraction Discrete wavelet transforms Color Image color analysis Visualization Mathematical models detection algorithms Image coding Computational modeling Robustness

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A Novel Feature Encoding Scheme for Machine Learning Based Malware detection Systems

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IEEE ACCESS 2024年 12卷 91187-91216页

作者： Das, Vipin Nair, Binoy B. Thiruvengadathan, Rajagopalan Amrita Vishwa Vidyapeetham Amrita Sch Artificial Intelligence Coimbatore 641112 India Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore 641112 India Southern Utah Univ Dept Engn & Technol Cedar City UT 84720 USA

Malware detection is an ever-evolving area given that the strides in the detection capabilities being matched by radical attempts to bypass the detection. As the sophistication of malware continues to increase, the demand for innovative approaches to improve detection capabilities become paramount. Machine learning/Deep learning models are being increasingly used for Malware detection, however one of the most important and frequently overlooked aspects of building such models is feature encoding. This research paper explores the importance of feature encoding to improve the efficiency of threat detection and proposes a novel entropy-based encoding scheme for the categorical features present in the data extracted from malicious inputs. The KDDCUP99, UNSW-NB15 and CIC-Evasive-PDFMal2022 datasets have been used to evaluate the effectiveness of the proposed encoding scheme. The results of the proposed encoding scheme are validated against seven other encoding schemes to ascertain the credibility and usability of the proposed scheme. The efficiency of the proposed system evaluated by applying different encoded versions of the datasets to train various machine learning models and determining the classification performance of the models on each dataset. The machine learning models trained with the proposed encoding scheme produced stable classification results and outperformed other encoding schemes when dimensionality reduction was applied on the data. The ensemble classifier trained using the proposed scheme was able to classify the data with an F1 score of 99.99% when the dimension-reduced entropy-encoded KDD Cup99 dataset was used to build the model. On the CIC-Evasive-PDFMal2022 dataset, the entropy encoding has exhibited a slightly improved classification parameters with the ensemble methods yielding a peak F1 score of 99.27%. We have also determined the feature importance values of the features present in the datasets to study the change in the contribution levels o

关键词： Malware Codes Encoding Machine learning Feature extraction Grippers Static analysis Computer security Intrusion detection Classification algorithms detection algorithms Cybersecurity categorical encoding intrusion detection machine learning malware classification malware detection

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Small Object detection Algorithm Based on Improved YOLOv8 for Remote Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 1734-1747页

作者： Yi, Hao Liu, Bo Zhao, Bin Liu, Enhai Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Chinese Acad Sci Key Lab Sci & Technol Space Optoelect Precis Measu Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bidirectional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top-down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground-truth box. Experimental results on NWPU VHR-10, RSOD, and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters, and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.

关键词： Attention mechanism detection algorithms remote sensing vision transformer YOLOv8

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ChaSAM: An Architecture Based on Perceptual Hashing for Image detection in Computer Forensics

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IEEE ACCESS 2024年 12卷 104611-104628页

作者： Dos Santos, Hericson Martins, Tiago Dos Santos Barreto, Jorge Andre Domingues Nakamura, Luis Hideo Vasconcelos Ranieri, Caetano Mazzoni De Grande, Robson E. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Sao Paulo Sci Police BR-16018120 Aracatuba Brazil Sao Paulo State Police BR-15015400 Sao Paulo Brazil Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos Brazil Fed Inst Sci Educ & Technol Sao Paulo IFSP BR-15808305 Catanduva Brazil Sao Paulo State Univ UNESP Inst Geosci & Exact Sci BR-13506692 Rio Claro Brazil Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada State Univ Southwest Bahia UESB Dept Exact & Technol Sci BR-45083900 Vitoria Da Conquista Brazil

The growing prevalence of digital crimes, especially those involving Child Sexual Abuse Material (CSAM) and revenge pornography, highlights the need for advanced forensic techniques to identify and analyze illicit content. While cryptographic hashing is commonly used in computer forensics, its effectiveness is often challenged because criminals can modify original information to create a new cryptographic hash. Perceptual hashes address this problem by focusing on the visual identity of the file rather than its bit-by-bit representation. This study introduces ChaSAM Forensics, a methodology that efficiently identifies illicit material using perceptual hashing techniques to track and identify illicit content, with a focus on child abuse material. Two new perceptual hashing algorithms, chHash and domiHash, were designed for integration into ChaSAM. The results showed that, under the tested conditions, the proposed chHash algorithm was more accurate than the established pHash algorithm when applied in a single iteration. Combinations of algorithms in two iterations were also assessed.

关键词： Forensics Cryptography Hash functions Computer architecture Media Hamming distances Computer crime Image analysis detection algorithms Forensic computing perceptual hashing image detection similarity

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A Chained Deep Learning Model for Fine-Grained Cyberbullying detection With Bystander Dynamics

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IEEE ACCESS 2024年 12卷 105588-105604页

作者： Saleh Alfurayj, Haifa Lebai Lutfi, Syaheerah Perumal, Ramesh Qassim Univ Coll Engn Buraydah 52571 Al Qassim Saudi Arabia Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Penang Malaysia Intel Microelect M Sdn Bhd Bayan Lepas 11900 Penang Malaysia

Accurate detection of cyberbullying on Social Networking Sites (SNSs) is crucial for online safety, especially for individuals impacted by it. Cyberbullying language is often implicit, necessitating a comprehensive analysis of conversational context to determine intention and severity. Current studies on cyberbully detection predominantly focus on the main post, overlooking the valuable insights provided by bystander reactions. Moreover, confusion over the differences between cyber-aggression and cyberbullying can undermine the reliability of some of these studies. To ameliorate these issues, this paper addresses the gap in existing research by emphasizing the significance of bystander in fine-grained cyberbullying detection. We specifically investigate the influence of bystander for more precise identification of cyberbullying attacks. Our approach involves fine-tuning a pre-trained language model (BERT) to identify features associated with bystander, categorizing them into roles such as defender, instigator, neutral, and other. To enhance fine-grained cyberbullying detection, we propose a classifier chain that combines BERT's output with Long Short-Term Memory (LSTM) networks. Our experiments demonstrate that involving bystander roles increases the model's performance by 25.35%, achieving a final classification F1-score of 89%. Moreover, our approach accurately determines the level of aggression in cyberbullying incidents through considering the interdependency of bystander roles and cyberbullying classes labels, providing an innovative solution to the challenge of cyberbullying misclassification. In summary, our study highlights the significance of incorporating bystanders as a feature in cyberbullying detection and introduces a chained fine-grained model that surpasses conventional approaches, demonstrating promising outcomes in precisely classifying cyberbullying incidents.

关键词： Cyberbullying Deep learning Context modeling Feature extraction Message systems Solid modeling Predictive models Long short term memory detection algorithms Social networking (online) Aggression bystander BERT cyberbullying detection chain deep learning LSTM

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E³-UAV: An Edge-Based Energy-Efficient Object detection System for Unmanned Aerial Vehicles

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 4398-4413页

作者： Suo, Jiashun Zhang, Xingzhou Shi, Weisong Zhou, Wei Yunnan Univ Engn Res Ctr Cyberspace Kunming 650091 Peoples R China Yunnan Univ Sch Software Kunming 650091 Peoples R China Chinese Acad Sci Inst Comp Technol Res Ctr Distributed Syst Beijing 100190 Peoples R China Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA

Motivated by the advances in deep learning techniques, the application of unmanned aerial vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then, we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.

关键词： Object detection Energy consumption Task analysis Image edge detection Autonomous aerial vehicles Detectors detection algorithms Edge computing edge intelligence energy efficiency object detection system unmanned aerial vehicle (UAV)

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You-Only-Look-Once Multiple-Strategy Printed Circuit Board Defect detection Model

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IEEE MULTIMEDIA 2024年第1期31卷 76-87页

作者： Wang, Xin Zhang, Hongyan Liu, Qianhe Gong, Wei Bai, Song You, Hezhen Shenyang Jianzhu Univ Sch Elect & Control Engn Shenyang 110168 Peoples R China Tongji Univ Comp Sci & Technol Dept Shanghai 200092 Peoples R China

Addressing the challenges of complex backgrounds, minute defects, and irregular shapes in PCB defect images that often lead to missed detections, inaccurate localizations, and false positives, this article introduces an improved you only look once (YOLO) model, termed YOLO-Biformer, to enhance the network's ability to detect surface defects on PCBs. First, YOLO-Biformer incorporates a hybrid attention module to differentiate the importance among various channels, thus strengthening the extraction of small target defect features and preventing the loss of minor target information often caused by deep convolutional networks. Second, the model introduces a jumping hollow space convolutional pyramid aimed at preserving more image details and interrelated information, thereby boosting the network's defect localization capability. Finally, the Enhanced Intersection Over Union-FocalLoss loss function is employed to enhance the network's ability to distinguish between defects with similar features. Experimental results demonstrate that the proposed algorithm increases the mean average precision by 4.1%, showing excellent performance in recognizing small target defects on printed circuit board surfaces and achieving both high accuracy and real-time capabilities.

关键词： Feature extraction Computational modeling Transmission line matrix methods Surface treatment Production Convolutional neural networks Adaptive systems detection algorithms YOLO Printed circuits Location awareness Boosting Data mining

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Early detection and Prevention of Malicious User Behavior on Twitter Using Deep Learning Techniques

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第5期11卷 6649-6661页

作者： Sanchez-Corcuera, Ruben Zubiaga, Arkaitz Almeida, Aitor Univ Deusto Fac Engn & DeustoTech Bilbao 48007 Bizkaia Spain Queen Mary Univ London Social Data Sci Lab London E1 4NS England

Organized misinformation campaigns on Twitter continue to proliferate, even as the platform acknowledges such activities through its transparency center. These deceptive initiatives significantly impact vital societal issues, including climate change, thus spurring research aimed at pinpointing and intercepting these malicious actors. Present-day algorithms for detecting bots harness an array of data drawn from user profiles, tweets, and network configurations, delivering commendable outcomes. Yet, these strategies mainly concentrate on postincident identification of malevolent users, hinging on static training datasets that categorize individuals based on historical activities. Diverging from this approach, we advocate for a forward-thinking methodology, which utilizes user data to foresee and mitigate potential threats before their realization, thereby cultivating more secure, equitable, and unbiased online communities. To this end, our proposed technique forecasts malevolent activities by tracing the projected trajectories of user embeddings before any malevolent action materializes. For validation, we employed a dynamic directed multigraph paradigm to chronicle the evolving engagements between Twitter users. When juxtaposed against the identical dataset, our technique eclipses contemporary methodologies by an impressive 40.66% in F score (F1 score) in the anticipatory identification of harmful users. Furthermore, we undertook a model evaluation exercise to gauge the efficiency of distinct system elements.

关键词： Social networking (online) Blogs Fake news Climate change Chatbots Social factors detection algorithms Crowdsourcing Malware Data integrity Information integrity Foreseeing malicious users social networks Twitter

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Remote Sensing Image Change detection Based on General Deep Forest Module

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IEEE ACCESS 2024年 12卷 84815-84829页

作者： Xu, Gang Zhang, Yanqin Mo, Jian Chen, Yiming Wang, Yunge Wang, Peng Zhejiang Coll Secur Technol Wenzhou 325016 Peoples R China Wenzhou Future City Res Inst Wenzhou 325016 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 210016 Peoples R China Hunan Inst Geol Disaster Invest & Monitoring Changsha 416099 Peoples R China Natl Dam Safety Engn Res Ctr Wuhan 430079 Peoples R China

Remote sensing image change detection, as one of the important branches of remote sensing technology, has been greatly developed and improved in recent years. The effectiveness of change detection is unstable, as deep learning components exhibit a high degree of sensitivity to hyperparameters, necessitating substantial fine-tuning to achieve optimal outcomes. Additionally, large and intricate deep learning models are not appropriate for smaller-scale datasets. In this paper, a remote sensing image change detection method based on general deep forest module (GDFM) is proposed. In the proposed GDFM, the common remote sensing image change detection method is firstly used for preliminary detection to obtain the detection result to be optimized, and the optimized change detection result is then obtained by using the classification function of the deep forest. This work aims to achieve a general enhancement of the performance of existing change detection methods by utilizing the characteristics of multi-grained scanning in deep forest and the data classification capabilities of cascaded forests. At the same time, this model alleviates the problems of parameter tuning complexity and inapplicability to small datasets in additional deep learning modules. It is worth noting that this general method is not a direct splicing process, but combines the advantages of the two parts and uses the characteristics of the deep forest to achieve the goal. Experiments are conducted on multiple common remote sensing change detection models. The results show that the proposed GDFM has significant improvements in metrics, which include varying degrees of improvement in F1 score by around 1% -14%.

关键词： Remote sensing Feature extraction Random forests Deep learning Regression analysis Monitoring Accuracy detection algorithms Remote sensing image change detection deep forest deep learning

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