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Deep Ensemble learning Approach for Face Anti-Spoofing Detection based on Pre-trained Models

Deep Ensemble Learning Approach for Face Anti-Spoofing Detec...

引用

2024 IEEE international conference on Computer Vision and machine Intelligence, CVMI 2024

作者： Vannurswamy, K. Shekar, B.H. Pilar, Bharathi Karunakar Kotegar, A. Jiang, Frank Mangalore University Dept of Computer Science Mangalore India Mangalore University Konaje Dept of Computer Science Mangalore India University College Mangalore Dept of Computer Science Mangalore India MAHE Manipal Dept of Data Science Manipal India Deakin University School of Information Tech Melbourne Australia

ISBN: (纸本)9798350376876

In this paper, we propose a Weighted Deep Ensemble learning (WDEL) to increase the overall accuracy of face anti-spoofing model by exploiting multiple learning architectures. Current anti-spoofing models based on one deep learning architecture are not able to extract all important information for discriminating real and fake faces. We therefore design a framework that fuses the information of five pre-trained deep learning models, Inception, Xception, VGG16, ResNet50 and MobileNet to create a high-quality feature representation against spoofing attacks. Moreover, we apply a weighted voting mechanism with each weight being reliant on the corresponding classifier's performance from real and spoof classification considering different metrics: precision for the non-spoof classification and recall for the spoof classification. The final prediction is obtained by aggregating these multiple classifiers using their optimal weights in an ensemble fashion. Additionally, we provide an experiment comparing the performance of these aforementioned pre-trained deep learning models. The comparison reveals that MobileNet, Xception, and ResNet50 exhibit the highest recall for spoofing and precision for real images, along with improved F1 score values in detection. This establishes them as optimal choices for facial spoofing detection. © 2024 IEEE.

关键词： Federated learning

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Intrusion Detection System Based on FastICA and Multi-Grained Cascaded Forest

Intrusion Detection System Based on FastICA and Multi-Graine...

引用

2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Fei, Jiahui Zhang, Shuangquan Lian, Zhichao School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781665410205

With the advancement of big data, microproces-sors, and other applications, the Internet of Things (IoT) has seen significant development. Owing to the lack of necessary security defense mechanisms, IoT devices are susceptible to being targeted and controlled by attackers. They can manipu-late a vast array of IoT devices to launch DDoS attacks on the network infrastructure of a country or region, leading to serious economic losses and social security risks. Intrusion detection methods based on deep learning generally rely on numerous high-quality training instances, making it difficult to apply them to network traffic lacking sufficient labeled data. Traditional machine learning (ML) methods have limited capabilities in extracting and representing features from high-dimensional data, making it challenging to discover underlying structures and patterns in the data. To address the above issues, this paper proposes an intrusion detection system (IDS) that integrates the Fast Independent Component Analysis (FastICA) module and the multi-Grained Cascade Forest (GcForest). By utilizing FastICA to preprocess the raw data and extract significant features, as well as improving the model's feature extraction capabilities with the aid of cascade modules. Experiments have indicated that FastICA-GcF achieves the binary classification accuracy of 99.95% on CIC-DDoS-2019, and the multiclass classification accuracy of 98.77%, outperforming existing DDoS attack detection models. © 2024 IEEE.

关键词： Network intrusion

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Multiloop Incremental Bootstrapping for Low-Resource machine Translation 19

Multiloop Incremental Bootstrapping for Low-Resource Machine...

引用

19th machine Translation Summit, MT Summit 2023

作者： Liu, Wuying Li, Wei Wang, Lin Shandong Key Laboratory of Language Resources Development and Application Ludong University Yantai264025 China Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies Guangzhou510420 China School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai200083 China

Due to the scarcity of high-quality bilingual sentence pairs, some deep-learning-based machine translation algorithms cannot achieve better performance in low-resource machine translation. On this basis, we are committed to integrating the ideas of machine learning algorithm improvement and data augmentation, propose a novel multiloop incremental bootstrapping framework, and design the corresponding semi-supervised learning algorithm. This framework is a meta-frame independent of specific machine translation algorithms. This algorithm makes full use of bilingual seed data of appropriate scale and super-large-scale monolingual data to expand bilingual sentence pair data incrementally, and trains machine translation models step by step to improve the translation quality. The experimental results of neural machine translation on multiple language pairs prove that our proposed framework can make use of continuous monolingual data to raise itself. Its effectiveness is not only reflected in the easy implementation of state-of-the-art low-resource machine translation, but also in the practical option to quickly establish precise domain machine translation systems. © 2023 The authors. This article is licensed under a Creative Commons Attribution 4.0 international License (CC BY 4.0)

关键词： learning algorithms

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Research on sentiment recognition in online shopping reviews using an integrated BERT and BiLSTM approach 4

Research on sentiment recognition in online shopping reviews...

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4th international conference on Internet of Things and Smart City, IoTSC 2024

作者： Huang, Jianrong Li, Guohua Li, Guowei Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China

ISBN: (纸本)9781510681767

This study explores a method integrating BERT and BiLSTM technologies aimed at enhancing the accuracy of sentiment recognition in online shopping reviews. Through detailed experimental validation, the fused model significantly surpasses traditional approaches that solely rely on either BERT or BiLSTM across various key performance metrics. This work not only confirms the potential of deep learning techniques in understanding complex textual emotional expressions but also provides new research directions and practical application possibilities for sentiment analysis of social media data. © 2024 SPIE.

关键词： Deep learning

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Probing hadronic cross sections in the TeV - PeV regime with DAMPE through machine learning techniques 38

Probing hadronic cross sections in the TeV - PeV regime with...

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38th international Cosmic Ray conference, ICRC 2023

作者： Coppin, Paul Tykhonov, Andrii Kotenko, Andrii Ruina, Arshia Stolpovskiy, Mikhail Département de physique nucléaire et corpusculaire Université de Genève École de physique Quai Ernest-Ansermet 24 Genève1205 Switzerland

Thanks to its large calorimeter, the DArk Matter Particle Explorer (DAMPE) satellite experiment is ideally suited for the direct detection of cosmic rays (CRs) up to the knee. At these TeV to PeV energies, the main uncertainty on the CR flux measurements comes from the hadronic cross sections, which are largely experimentally unconstrained. We developed novel machine learning (ML) tools that are able to probe the depth at which CRs inelastically interact inside the DAMPE experiment. Applying these techniques to 7 years of DAMPE data, and comparing the results to predictions made by CR simulation frameworks such as Geant4 and FLUKA, we demonstrate how DAMPE data can be used to constrain the hadronic cross sections. Our results thus provide an important step towards reducing the uncertainties of CR flux measurements. Additionally, they form a pathfinder for similar studies with future experiments. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Compression of Structured data with Autoencoders: Provable Benefit of Nonlinearities and Depth 41

Compression of Structured Data with Autoencoders: Provable B...

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41st international conference on machine learning, ICML 2024

作者： Kögler, Kevin Shevchenko, Alexander Hassani, Hamed Mondelli, Marco ISTA Klosterneuburg Austria Department of Electrical and Systems Engineering University of Pennsylvania United States

Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shallow two-layer setting. In particular, to what degree does a shallow autoencoder capture the structure of the underlying data distribution? For the prototypical case of the 1-bit compression of sparse Gaussian data, we prove that gradient descent converges to a solution that completely disregards the sparse structure of the input. Namely, the performance of the algorithm is the same as if it was compressing a Gaussian source - with no sparsity. For general data distributions, we give evidence of a phase transition phenomenon in the shape of the gradient descent minimizer, as a function of the data sparsity: below the critical sparsity level, the minimizer is a rotation taken uniformly at random (just like in the compression of non-sparse data);above the critical sparsity, the minimizer is the identity (up to a permutation). Finally, by exploiting a connection with approximate message passing algorithms, we show how to improve upon Gaussian performance for the compression of sparse data: adding a denoising function to a shallow architecture already reduces the loss provably, and a suitable multi-layer decoder leads to a further improvement. We validate our findings on image datasets, such as CIFAR-10 and MNIST. Copyright 2024 by the author(s)

关键词： Gaussian distribution

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Causal Effect Identification in LiNGAM Models with Latent Confounders 41

Causal Effect Identification in LiNGAM Models with Latent Co...

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41st international conference on machine learning, ICML 2024

作者： Tramontano, Daniele Kivva, Yaroslav Salehkaleybar, Saber Drton, Mathias Kiyavash, Negar Technical University of Munich Munich Germany Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Leiden Institute of Advanced Computer Science Leiden University Netherlands Munich Center for Machine Learning Munich Germany

We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects. Copyright 2024 by the author(s)

关键词： Graph algorithms

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Improved R3D for Human Action Recognition Based on Transfer learning 6

Improved R3D for Human Action Recognition Based on Transfer ...

引用

6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Han, Xiaowei Guo, Qiuyang Lu, Yunjing Liu, Jiwu Fei, Chaolong Harbin University of Commerce Postdoctoral research workstation of Northeast Asia Service Outsourcing Research Center Station of Applied Economics Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbi China Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China Guangdong Technology College Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Zhaoqing China

ISBN: (纸本)9798331527426

Human action recognition is an important research direction in the field of computer vision and machine learning, which is widely used in intelligent surveillance, human-computer interaction, sports analysis and other fields. Traditional action recognition methods usually rely on large-scale labeled datasets and complex feature extraction processes, which have problems such as high data acquisition cost and long model training time, as an effective solution, transfer learning can significantly improve the performance of the model and reduce the dependence on labeled data by transferring the features learned by the pre-trained model on large-scale datasets to the target task. In this paper, the R3D model is used as the backbone network, and the model is improved by strengthening the channel and temporal feature extraction capabilities. The model achieves good performance on Shopping Action_ v l by transfer learning based on HMDB51 and UCF101 datasets, and obtains 89.53% and 94.14% of the optimal Top-1 and Top-5 indicators after pre-training on UCF101 dataset. In summary, the C-TSAB ResNets model is proposed in this paper performs well in accuracy and robustness on human action recognition tasks through transfer learning, which verifies the actual effect of the innovation in the research work of this topic. © 2024 IEEE.

关键词： Convolutional neural networks

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Federated learning with Class Balanced Loss Optimized by Implicit Stochastic Gradient Descent 7th

Federated Learning with Class Balanced Loss Optimized by Imp...

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7th international conference on Soft Computing in data science (SCDS)

作者： Zhou, Jincheng Zheng, Maoxing Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Peoples R China

ISBN: (纸本)9789819904044;9789819904051

Federated learning is a paradigm for distributed machine learning in which a central server interacts with a large number of remote devices to create the optimal global model. System and data heterogeneity are now the two largest impediments to federated learning. This work suggests a federated learning strategy based on stochastic gradient descent optimization as a solution to the problem of heterogeneity-induced slow convergence, or even non-convergence, of the global model. This work estimates the average global gradient using locally uploaded model parameters without computing the first derivative or updating global model parameters through gradient descent. Allowing the global model to be used with fewer communication rounds. Obtain faster and more reliable convergence results. In experiments simulating varying degrees of heterogeneous settings, the strategy proposed in this work delivered faster and more stable convergence than FedProx and FedAvg. This work offers a strategy that decreases the number of communication cycles on highly heterogeneous synthetic datasets by around 50% compared to FedProx, therefore considerably enhancing the stability and durability of federated learning.

关键词： Fast convergence Optimization algorithm System heterogeneity data heterogeneity Implicit stochastic gradient descent Global model Central server Distributed machine learning Federated learning

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Traffic Pattern Division Based on Mobile Signaling data 24

Traffic Pattern Division Based on Mobile Signaling Data

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24th COTA international conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Yu, Luyi Zhu, Xun Zhou, Shasha Wang, Jingyuan Ningbo Zhenhai Urban Planning & Survey Research Institute Ningbo China College of Civil and Transportation Engineering Shenzhen Univ. Shenzhen China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

In order to solve the problem of dividing the traffic pattern of the OD extracted by the mobile phone signaling, using resident survey data and network crawling data as samples, features such as travel attributes and traveler attributes are extracted and equivalently processed to construct model input data equivalent to mobile phone signaling data. Selecting random forest, support vector machine, and neural network machine learning model helps divide the three modes of transportation: car travel, public transportation, and other travel modes. Through multi-index evaluation and case verification, the results show that the random forest model is a better overall model with a prediction accuracy of 70%. The prediction results are stable and reliable and have certain practical application value. © ASCE.

关键词： Random forests

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