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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Virtual Keyboard:A Real-Time Hand Gesture Recognition-Based Character Input System Using LSTM and Mediapipe Holistic

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computer Systems Science & engineering 2024年第2期48卷 555-570页

作者： Bijon Mallik Md Abdur Rahim Abu Saleh Musa Miah Keun Soo Yun Jungpil Shin Department of Computer Science and Engineering Pabna University of Science and TechnologyPabna6600Bangladesh School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan School of Computer and Information Technology Ulsan CollegeDong-guUlsan44610South Korea

In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and cameras,often caused by environmental *** issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and *** study presents a novel virtual keyboard allowing character input via distinct hand gestures,focusing on two key aspects:hand gesture recognition and character input *** developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture *** also integrated CNN,max-pooling,and dropout layers for improved spatial feature *** model architecture processes both temporal and spatial aspects of hand gestures,using LSTM to extract complex patterns from frame sequences for a comprehensive understanding of input *** unique dataset,essential for training the model,includes 1,662 landmarks from dynamic hand gestures,33 postures,and 468 face landmarks,all captured in real-time using advanced pose *** model demonstrated high accuracy,achieving 98.52%in hand gesture recognition and over 97%in character input across different *** excellent performance in real-time testing underlines its practicality and effectiveness,marking a significant advancement in enhancing human-device interactions in the digital age.

关键词： Hand gesture recognition M.P.holistic open CV virtual keyboard LSTM human-computer interaction

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Beyond boundaries: Unifying classification and segmentation in wildfire detection systems

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Multimedia Tools and Applications 2024年 1-33页

作者： Singh, Swapnil Vazirani, Vidhi Singhania, Sanvika Suroth, Vaishnavi Singh Soni, Vaibhav Biwalkar, Ameyaa Krishnan, Deepa Computer Science Department Virginia Tech BlacksburgVA United States Computer Science Department Northwestern University EvanstonIL United States Computer Engineering Department Mukesh Patel School of Technology Management & ampEngineering NMIMS University Maharashtra Mumbai India

Forest fires, a dangerous natural phenomenon, cause large-scale destruction in forests and nearby communities. In this paper, we leverage the capabilities of classification and fast prediction of machine learning and deep learning to detect forest fires by proposing a dual-stage approach of identification and segmentation of burnt/burning regions. The initial identification step uses simple classification by harnessing custom convolutional neural network and AlexNet to detect satellite images displaying signs of burning, followed by the next step of segmentation, which employs UNet and SegNet architectures to precisely localize the burnt/burning areas. We chose UNet considering its ability of efficient feature preservation, and SegNet prominent use for precise boundary delineation. For training our models, we have curated a dataset using Google Earth Engine, querying latitude, longitude, and dates. Image processing techniques facilitated the extraction of segmentation masks, bounding box images, and segmented images. The post-training results portray that CNN outperforms AlexNet with an 88.19% accuracy for classification, while in segmentation, UNet excelled over SegNet with a dice score of 0.6869. If deployed, this study stands to benefit forest officials, local communities, and biodiversity. Moreover, it aids afforestation teams in identifying burnt zones for targeted reforestation efforts, ensuring a more effective response to forest fires. © The Author(s) 2024.

关键词： Landsat

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The Smart and Secured Communication Using Applied Cryptography Algorithms 5th

The Smart and Secured Communication Using Applied Cryptograp...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Jebakumari, S. Adlin Hemlata Ayesha, Amreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the implementation of a at ease communication device the usage of an implemented cryptography algorithm. The gadget is designed to provide comfy statistics and voice communique between endpoints using techniques consisting of encryption and digital signature. The applied cryptography set of rules includes primary components, the symmetric and the uneven algorithms. The symmetric algorithms, along with AES and DES, are used for statistics encryption, even as the uneven algorithms, which include RSA and DSA, are used for virtual signature. The proposed gadget additionally focuses on security of physical places to defend the important thing exchange by means of the use of physical access control measures which include biometric identification. The system additionally employs advanced safety strategies together with message authentication codes and protection protocols like SSL/TLS. The device is examined for robustness and safety using real communique experiments. The results confirm that the proposed machine is cozy and green in supplying comfortable communication among endpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

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International Journal of computers and Applications 2024年第7期46卷 496-507页

作者： Inje, Bhushan Nagwanshi, Kapil Kumar Rambola, Radhakrishna Amity School of Engineering and Technology Amity University Jaipur India NMIMS University MPSTME Shirpur Shirpur Dist Dhule India Department of Computer Science and Engineering SoS Engineering and Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based Clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.4

关键词： Clustering algorithms

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GMTS: GNN-based multi-scale transformer siamese network for remote sensing building change detection

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International Journal of Digital Earth 2023年第1期16卷 1685-1706页

作者： Xinyang Song Zhen Hua Jinjiang Li School of Infomation and electronic engineering Shandong Technology and Business UniversityInstitute of Network Technology(ICT)YantaiPeople's Republic of China School of Computer Science and Technology Shandong Technology and Business UniversityYantaiPeople's Republic of China

With the remarkable success of change detection(CD)in remote sensing images in the context of deep learning,many convolutional neural network(CNN)based methods have been *** the current research,to obtain a better context modeling method for remote sensing images and to capture more spatiotemporal characteristics,several attention-based methods and transformer(TR)-based methods have been *** research has also continued to innovate on TR-based methods,and many new methods have been *** of them require a huge number of calculation to achieve good ***,using the TR-based mehtod while maintaining the overhead low is a problem to be ***,we propose a GNN-based multi-scale transformer siamese network for remote sensing image change detection(GMTS)that maintains a low network overhead while effectively modeling context in the spatiotemporal *** also design a novel hybrid backbone to extract *** with the current CNN backbone,our backbone network has a lower overhead and achieves better ***,we use high/low frequency(HiLo)attention to extract more detailed local features and the multi-scale pooling pyramid transformer(MPPT)module to focus on more global features ***,we leverage the context modeling capabilities of TR in the spatiotemporal domain to optimize the extracted *** have a relatively low number of parameters compared to that required by current TR-based methods and achieve a good effect improvement,which provides a good balance between efficiency and performance.

关键词： Remote sensing(RS) change detection(CD) depthwise over-parameterized convolutional(DO-Conv) attention mechanism transformer graph convolution

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Modeling of Complex Traffic Interaction Under Uncontrolled Intersection Scenario

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Li, Jiangkun Deng, Weiwen Wang, Ying Wang, Jian Ding, Juan School of Transportation Science and Engineering Beihang University Beijing China College of Computer Science and Technology Jilin Univerisity Changchun China PanoSim Technology Company Limited Jiaxing China

Complex traffic scenarios at uncontrolled intersections are crucial for the test validation of autonomous driving systems. The core of the test scenario construction lies in the accurate modeling of the complex interaction behaviors between vehicles in dynamic traffic. Data-driven models are difficult to support long-term simulation due to the existence of cumulative errors. In addition, existing mechanistic models usually assume rational driver behavior and focus mainly on improving efficiency and safety, thus simplifying vehicle interactions. To overcome the limitations of existing studies, we construct a complex traffic interaction model based on social force theory. This model captures the intricate interactions among vehicles at uncontrolled intersections by introducing the concepts of driving and repulsive forces. In particular, we propose a novel concept of segmented conflicting repulsion, an approach that can accurately model high-risk scrambling interactions between vehicles at intersections. Validation of the model using real data sets demonstrates its ability to accurately reproduce complex interaction behaviors at real-world intersections. Further, simulation analysis and application results reveal that our model-generated scenarios significantly outperform those created by SUMO in terms of complexity, thereby effectively enhancing the safety assessment of the autonomous driving system, Apollo. IEEE

关键词： Safety testing

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Deep Reinforcement Learning Based Reliable Data Transmission Scheme for Internet of Underwater Things in 5G and beyond Networks 2

Deep Reinforcement Learning Based Reliable Data Transmission...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India School of Computer Science and Artificial Intelligence SR University Anandsagar Hansaparthy Hanumakomda Telagana India

The Internet of Underwater Things (IoUT) is an interconnected communication ecosystem for underwater devices in maritime environments. IoUT devices range from seabed sensors to ships and boats in oceans which help in diverse practical applications of underwater information ranging from marine transportation, navigation, and underwater exploration to disaster prevention with intelligent monitoring. There exist significant obstacles within the present architecture of the IoUT. This article proposes three distinct methodologies aimed at enhancing the robustness and reliability of the communication architecture for 5G and future networks. Firstly, the employment of the Deep Reinforcement Learning approach is advocated for the identification of anomalies and faulty sensors. Secondly, energy consumption is identified as a crucial factor for IoUT devices. To address this concern, the integration of renewable energy sources, namely oxygen rechargeable batteries, with nano-generators is employed. This integration allows for the replenishment of the batteries through the harnessing of kinetic energy from water. Thirdly, the process by which information can be efficiently transmitted from IoUT sensors located on the seabed to the final terrestrial network is delineated, with an emphasis on minimizing the number of hops required. Additionally, a case study is presented, focusing on the mitigation of interference within the IoUT. Lastly, the article delves into the various research challenges and unresolved matters that persist within the realm of IoUT. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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DeBERTa-Based Social Media Sentiment Analysis: A Cryptocurrency Case Study

DeBERTa-Based Social Media Sentiment Analysis: A Cryptocurre...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Ye, Jiarui Biswas, Reetam Mishra, Nilamadhab Nanjing University of Technology School of Computer Science and Engineering Nanjing China Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India Vit Bhopal University Madhya Pradesh Sehore India

ISBN: (纸本)9798331508432

The emergence of cryptocurrencies has dramatically impacted the financial sector, drawing significant attention and sparking widespread debates across platforms like Facebook and Reddit. These discussions offer crucial insights into public sentiment, which uniquely influences cryptocurrency valuations, setting them apart from traditional financial products. Due to this sensitivity to public opinion, both the academic and financial sectors are closely monitoring these dynamics. This paper utilizes Natural Language Processing (NLP) technologies, specifically the DeBERTa model, to perform an in-depth sentiment analysis of cryptocurrency-related discussions on Reddit. Our methodology classifies sentiments into five levels - very positive, positive, neutral, negative, and very negative - allowing for a precise assessment of public emotions and acceptance of cryptocurrencies. The findings provide vital data for market analysis and underscore the significant influence of public sentiment on the cryptocurrency markets. © 2024 IEEE.

关键词： Decentralized finance

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