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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Chawla, Shourya Abuzer, Mohd. Singh, Abhinav Sharma, Dolly Amity University Amity School of Engineering and Technology Department of Computer Science Noida India

ISBN: (纸本)9798350344387

A phone number is a personal unique number which can identify its owner. Personal information can be exploited through a person's phone number. Many methods such as phishing and spam attacks have been used to exploit a person's personal information. Nowadays, a phone number has many linked important information such as bank accounts, and passwords which make it more vulnerable to attacks. The growing demand for accessibility and connectivity is creating demand for an efficient solution. Hence, a caller identification app which can identify caller ID and spam protection of the incoming call or SMS is required. It protects users from attending unwanted calls and messages. In this work, the design and working of a caller identification app using AWS services is explored. Many researchers have conducted studies and surveys to identify the process of caller identification and develop caller identification methods and have published their research about the same. Most of these methods are conventional. What makes the proposed model novel, is that it uses a cloud-based new age model with complete backend design to develop a strong caller identification application with its roots in the AWS cloud. Though other models have been successful, this model is better because of the benefits provided by the cloud, which include high availability, scalability, elasticity, security, and cost-friendliness. © 2023 IEEE.

关键词： Caller identification Phishing spam attacks unwanted calls vulnerable

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GDMNet: A Unified Multi-Task Network for Panoptic Driving Perception

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computers, Materials & Continua 2024年第8期80卷 2963-2978页

作者： Yunxiang Liu Haili Ma Jianlin Zhu Qiangbo Zhang School of Computer Science and Information Engineering Shanghai Institute of TechnologyShanghai201418China

To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object ***,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient ***,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training *** results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,*** detection performance surpasses that of other single-task or multi-task algorithm models.

关键词： Autonomous driving multitask learning drivable area segmentation lane detection vehicle detection

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A secure double spectrum auction scheme

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Digital Communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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Enhance the Performance of Directional Feature-based Palmprint Recognition by Directional Response Stability Measurement

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Machine Intelligence Research 2024年第3期21卷 597-614页

作者： Haitao Wang Wei Jia School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China

Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.

关键词： Biometrics palmprint recognition directional response stability directional coding-based methods directional feature

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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Ancient Character Recognition: A Comprehensive Review

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IEEE Access 2025年 13卷 88847-88857页

作者： Krithiga, R. Varsini, S.R. Joshua, R. Gabriel Om Kumar, C.U. Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic. © 2013 IEEE.

关键词： computer vision

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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Synergizing Artistry and technology by Unveiling the Integration of Matte Painting Techniques in Crafting Precise and Immersive Visual Effects Backgrounds 4th

Synergizing Artistry and Technology by Unveiling the Integra...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Raturi, Vinod Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Animation and Game Design Amity School of Engineering and Technology Amity University Punjab India

ISBN: (纸本)9789819752300

The film industry has evolved because of visual effects (VFX). Matte painting is a component of VFX and is important to VFX. Matte painting is a successful method for producing realistic visual effects and backgrounds for movies and television. To produce a scene or setting that can be utilized as a background in a movie or television show, this technique entails painting on a flat surface, such as a canvas or board. Filmmakers can depict a setting that is not actually there with the use of matte painting. The set will cost a lot of money to produce. This makes it possible to carry out actions that aren’t possible at the shooting site. This study paper’s aim is to investigate the background, methods, and applications of matte painting in the film and television industries. This essay also looks at the benefits and drawbacks of matte painting as well as its efficiency in producing realistic visual effects backdrops. A single work of art can now incorporate digital, photographic, and computer-generated imagery, providing artists with infinite options to create unique and cutting-edge pieces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Television applications

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Fedchain: Decentralized Federated Learning and Blockchain-Assisted System for Sustainable Irrigation

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Bera, Somnath Dey, Tanushree Mukherjee, Anwesha Bhattacharya, Pronaya De, Debashis Maulana Abul Kalam Azad University of Technology West Bengal India Mahishadal Raj College Mahishadal West Bengal India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University Kolkata West Bengal India

The conventional Internet of Things-based soil moisture monitoring system for irrigation decision making suffers from huge network traffic, high latency and energy consumption, and compromise in data security. To overcome these challenges, this paper proposes a blockchain-assisted decentralized federated learning strategy, Fedchain for irrigation decision making based on edge-cloud computing. A peer-to-peer network is formed among the edge servers. The edge servers have their local datasets, which are analyzed locally using Long Short-Term Memory (LSTM) network, and the model parameters are exchanged between the peer nodes. The model is updated accordingly. For data security purposes, blockchain is used. The LSTM model updates are recorded in the InterPlanetary File System by the local user, and a unique Content Identifier is generated for data retrieval, and it is stored in the blockchain as a transaction. A Distributed Hash Table in the file system maps the Content Identifier in the blockchain to the stored data in the file system, ensuring effective retrieval. The results show that Fedchain achieves above ~99% prediction accuracy, and reduces latency and energy consumption by 78% than the edge-cloud framework without federated learning. The use of blockchain reduces the mining cost by ~78% than the competing methods. IEEE

关键词： Blockchain

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Zero Knowledge based Authentication for Internet of Medical Things 14

Zero Knowledge based Authentication for Internet of Medical ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Misha, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Amity University Uttar Pradesh Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Pranveer Sing Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350335095

Internet of Things (IoT) applications, such as e-healthcare departments have grown tremendously where devices gather patient data and instantly transmit it over a distance to servers. Despite its huge advantages, IoT in the healthcare sector has acquired little consideration, largely because of the threats of unauthorized access to private health data made possible by the weak wireless communication channel. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the Internet of medical things (IoMT) that is based on zero-knowledge proofs (ZKP). The research will include the comparison between computational delays in traditional protocols and ZKP protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP. © 2023 IEEE.

关键词： Authentication

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