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Privacy-Preserving Multi-soft Biometrics through Generative Adversarial Networks with Chaotic Encryption

Privacy-Preserving Multi-Soft Biometrics through Generative ...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Hongying Zheng Hongdie Li Di Xiao Maolan Zhang School of Computer Science Chongqing University Chongqing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The rapid advances in deep learning have enabled the extraction of soft-biometric attributes from faces, raising privacy concerns regarding images collected for face recognition. To address this issue, we propose a privacy-preserving scheme for multiple soft-biometric attributes using Generative Adversarial Networks (GANs), aiming to effectively protect individual privacy while maintaining face recognition system performance. Our scheme incorporates two main modules: 1) The attribute encryption system, which identifies attributes to be hidden through chaotic cryptography and supports the recovery of original attributes. 2) Encoder features are adaptively transformed using Selective Transmission Units (STUs) within a generator featuring an encoder-decoder structure to generate perturbed images. The generated perturbed images are visually similar to the original images but differ in soft-biometric attributes. Experimental results show that our method maintains high face recognition accuracy while preserving privacy, and the quality of the generated images surpasses existing techniques.

关键词： Privacy Accuracy Chaotic communication Face recognition System performance Speech recognition Generative adversarial networks Generators Encryption Protection

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Proceedings - 3rd international conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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ADHDNet: A DNN Based Framework for Efficient ADHD Detection from fMRI Dataset 9th

ADHDNet: A DNN Based Framework for Efficient ADHD Detection ...

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9th international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Chowdhury, Anjan Chatterjee, Rajdeep Aich, Geetanjali Ghosh, Kuntal ISI Kolkata Ctr Soft Comp Res Kolkata 700108 India KIIT Deemed Be Univ Sch Comp Engn Bhubaneswar 751024 India Heritage Inst Technol Dept Elect & Commun Engn Kolkata 700107 India

ISBN: (纸本)9783031126994;9783031127007

The study of functional connectivity is an evolving field of research in brain network-based analysis of neurological disorders. The interconnection between various brain regions is affected due to different neuro-disorders. Attention Deficit Hyperactivity Disorder (ADHD) has been studied using complex network-based features from the ADHD-200 competition fMRI dataset. The objective is to classify a typically developing subject from one showing a mature stage of acute symptoms (e.g., insufficient attention and/or hyperactivity). ADHD symptoms, being difficult to diagnose efficiently, will, if successfully detected computationally, lead to suitable clinical intervention and improved outcomes. The paper's novelty is to capture the change of functional connectivity between brain regions of interest (ROIs) due to the ADHD syndrome using the brain atlas (MSDL, BASC-64/444) and complex network measures. A novel 5-layered Deep Neural Network (ADHDNet) has been implemented in this paper for efficient computer-aided diagnosis of ADHD. The output is compared with the traditional and best-performing machine learning technique Gradient Boosting as the dataset used is imbalanced between control population and mature-ADHD patients. SMOTE, Random Under (RUS), and Over (ROS) Samplers have been employed to deal with the data imbalance. This study is unique in its focus on the efficient detection of ADHD cases using complex network concepts as the feature extractor. The best performing results are 100% and 93% test-accuracies from BASC-64 + RUS and MSDL + ROS, respectively. The proposed ADHDNet provides consistently excellent and stable results based on evaluation metrics such as F1-score, accuracy, and Area under the ROC Curve(AUC).

关键词： ADHD Classification Connectome Complex network fMRI Deep Neural Network Atlas

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

3rd International Conference on Emergent Converging Technolo...

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

ISBN: (纸本)9789819986453

The proceedings contain 56 papers. The special focus in this conference is on Emergent Converging Technologies and Biomedical Systems. The topics include: Smart Health Monitoring System for Elderly People;impact of Covid-19 and Subsequent Usage of IoT;design of Battery Monitoring System for Converted Electric Cycles;image Denoising Framework Employing Auto Encoders for Image Reconstruction;server Access pattern Analysis Based on Weblogs Classification Methods;multilingual Emotion recognition from Continuous Speech Using Transfer Learning;Violence Detection Using DenseNet and LSTM;financial Technology and Competitive Landscape in the Banking Industry of Bangladesh: An Exploratory Focus;on Parameterized Picture Fuzzy Discriminant Information Measure in Medical Diagnosis Problem;review on Deep Learning-Based Classification Techniques for Cocoa Quality Testing;a Curated Study on Machine Learning Based Algorithms and Sensors for Drone Technology in Various Application;automatic Detection of Coagulation of Blood in Brain Using Deep Learning Approach;deepPose: A 2D Image Based Automated Framework for Human Pose Detection and a Trainer App Using Deep Learning;Phylogenetic Study of Surface Glycoprotein (S1 Spike Protein) Sequence of SARS-CoV-2 Virus;pervasive and Wearable computing and Networks;power of Image-Based Digit recognition with Machine Learning;open-Source Gesture-Powered Augmented Reality-Based Remote Assistance Tool for Industrial Application: Challenges and Improvisation;enhancing Biometric Performance Through Mitigation of Sleep-Related Breaches;Neural Network Based CAD System for the Classification of Textures in Liver Ultrasound Images;fuzzy Vendor–Buyer Trade Credit Inventory Model-Pentagonal Numbers in Permissible Limits Delay in Account Settlement with Supervised Learning;a Comparative Survey on Histogram Equalization Techniques for Image Contrast Enhancement.

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Robust Knowledge Distillation from RNN-T Models with Noisy Training Labels Using Full-Sum Loss 48

Robust Knowledge Distillation from RNN-T Models with Noisy T...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zeineldeen, Mohammad Audhkhasi, Kartik Baskar, Murali Karthick Ramabhadran, Bhuvana Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany Google Llc New York United States

ISBN: (纸本)9781728163277

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data. © 2023 IEEE.

关键词： Recurrent neural networks

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5W1H Extraction With Large Language Models

5W1H Extraction With Large Language Models

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international Joint conference on Neural Networks (IJCNN)

作者： Cao, Yang Lan, Yangsong Zhai, Feiyan Li, Piji Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

The extraction of essential news elements through the 5W1H framework (What, When, Where, Why, Who, and How) is critical for event extraction and text summarization. The advent of Large language models (LLMs) such as ChatGPT presents an opportunity to address language-related tasks through simple prompts without fine-tuning models with much time. While ChatGPT has encountered challenges in processing longer news texts and analyzing specific attributes in context, especially answering questions about What, Why, and How. The effectiveness of extraction tasks is notably dependent on highquality human-annotated datasets. However, the absence of such datasets for the 5W1H extraction increases the difficulty of finetuning strategies based on open-source LLMs. To address these limitations, first, we annotate a high-quality 5W1H dataset based on four typical news corpora (CNN/DailyMail, XSum, NYT, RAMDS);second, we design several strategies from zero-shot/fewshot prompting to efficient fine-tuning to conduct 5W1H aspects extraction from the original news documents. The experimental results demonstrate that the performance of the fine-tuned models on our labelled dataset is superior to the performance of ChatGPT. Furthermore, we also explore the domain adaptation capability by testing the source-domain (e.g. NYT) models on the target domain corpus (e.g. CNN/DailyMail) for the task of 5W1H extraction.

关键词： 5W1H Extraction LLMs Prompting Fine-tuning

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Partial Discharge pattern recognition of Power Transformer by Distributed computing Framework

Partial Discharge Pattern Recognition of Power Transformer b...

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2022 IEEE international conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Jiang, Tianyan Yuan, Haoxiang Liu, Haicheng Zhu, Hanlin Chen, Xi Yang, Zhangang School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing400054 China State Grid Chongqing Shibei Electric Power Supply Branch Chongqing400023 China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

This paper presents a back propagation neural network (BPNN) pattern recognition algorithm based on distributed framework. In this paper, four typical partial discharge(PD) models of artificial oil-paper insulation defects are designed. PD signal characteristic parameters are extracted to form PD spectrum statistical feature library under pulsating DC voltage. This algorithm is applied to pattern recognition of four typical PD models. This algorithm is dissimilar from the traditional BPNN algorithm. The distributed BPNN algorithm can greatly reduce the training speed of the model, so as to achieve the purpose of rapid identification of fault modes. Compared with the traditional BPNN algorithm, the calculation speed of the algorithm is faster than that of the traditional algorithm. © 2022 IEEE.

关键词： Partial discharges

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pattern Inversion as a pattern recognition Method for Machine Learning 2

Pattern Inversion as a Pattern Recognition Method for Machin...

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Mikhailov, Alexei Karavay, Mikhail Institute for Control Problems Russian Academy of Sciences Moscow Russia

Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencing, etc. The paper discusses the use of indexing-based methods for pattern recognition. It is shown that for pattern recognition applications such indexing methods replace with inverse patterns the fully inverted files, which are typically employed in search engines. Not only such inversion provides automatic feature extraction, which is a distinguishing mark of deep learning, but, unlike deep learning, pattern inversion supports almost instantaneous learning, which is a consequence of absence of coefficients. The paper discusses a pattern inversion formalism that makes use on a novel pattern transform and its application for unsupervised instant learning. Examples demonstrate a view-angle independent recognition of three-dimensional objects, such as cars, against arbitrary background, prediction of remaining useful life of aircraft engines, and other applications. In conclusion, it is noted that, in neurophysiology, the function of the neocortical mini-column has been widely debated since 1957. This paper hypothesizes that, mathematically, the cortical mini-column can be described as an inverse pattern, which physically serves as a connection multiplier expanding associations of inputs with relevant pattern classes. © Published under licence by IOP Publishing Ltd.

关键词： Search engines

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Investigation of Machine Learning Algorithms for pattern recognition in Image Processing 5

Investigation of Machine Learning Algorithms for Pattern Rec...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 IEEE.

关键词： Feature extraction

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Automated Computer Aided Detection of Diabetic Retinopathy using Machine Learning Hybrid Model 13

Automated Computer Aided Detection of Diabetic Retinopathy u...

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13th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kubde, Aishwarya Mohod, Sharad W. Dept. of Electronic Engineering Prof. Ram Meghe Institute of Technolgy & Research Badnera Amravati India

ISBN: (纸本)9781713857938

Diabetic retinopathy is spreading dangerously worldwide among people with diabetes, leading to reduced vision and completely blindness. In this paper, a technique is proposed to identify diabetic retinopathy using the fundus image obtained from the patient's retina. The method involves processing the digital image on the fundus image, which help the ophthalmologist in examine DR. Micro-aneurysm, which is considered as first stage of diabetic retinopathy has been diagnosed using Neural Network. The proposed Support Vector Machine has been compared to existing methods - Naive Bayes classifier. Experimental validation is done with MATLAB/SIMULINK software. The preprocess image has been applied as input data for neural network pattern recognition. A significant improvement has been observed in terms of Sensitivity, Specificity and Accuracy compared to the aforementioned existing methods. © Grenze Scientific Society, 2022.

关键词： pattern recognition

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