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Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang
| Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang |
| Autore | Fan Lixin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (233 pages) |
| Disciplina | 005.82 |
| Altri autori (Persone) |
ChanChee Seng
YangQiang |
| Soggetto topico |
Machine learning
Data protection Image processing—Digital techniques Computer vision Image processing Machine Learning Data and Information Security Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 981-19-7554-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network Models -- Chapter 11. Model Auditing For Data Intellectual Property . |
| Record Nr. | UNISA-996546839603316 |
Fan Lixin
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang
| Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang |
| Autore | Fan Lixin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (233 pages) |
| Disciplina | 005.82 |
| Altri autori (Persone) |
ChanChee Seng
YangQiang |
| Soggetto topico |
Machine learning
Data protection Image processing—Digital techniques Computer vision Image processing Machine Learning Data and Information Security Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 981-19-7554-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network Models -- Chapter 11. Model Auditing For Data Intellectual Property . |
| Record Nr. | UNINA-9910728383303321 |
|
Fan Lixin
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Innovations in Data Analytics [[electronic resource] ] : Selected Papers of ICIDA 2022 / / edited by Abhishek Bhattacharya, Soumi Dutta, Paramartha Dutta, Vincenzo Piuri
| Innovations in Data Analytics [[electronic resource] ] : Selected Papers of ICIDA 2022 / / edited by Abhishek Bhattacharya, Soumi Dutta, Paramartha Dutta, Vincenzo Piuri |
| Autore | Bhattacharya Abhishek |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (633 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DuttaSoumi
DuttaParamartha PiuriVincenzo |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Computational intelligence
Quantitative research Image processing—Digital techniques Computer vision Data protection Telecommunication Computational Intelligence Data Analysis and Big Data Computer Imaging, Vision, Pattern Recognition and Graphics Data and Information Security Communications Engineering, Networks |
| Soggetto non controllato |
Computer Security
Telecommunication Engineering Computers Technology & Engineering |
| ISBN | 981-9905-50-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Contents -- About the Editors -- Computational Intelligence -- Transience in COVID Patients with Comorbidity Issues-A Systematic Review and Meta-Analysis Based on Indian and Southeast Asian Context -- 1 Introduction -- 1.1 Risk of COVID Patients with Comorbidity Issues -- 2 COVID Patient Statistics with Comorbidity Issues -- 3 Research Methodology -- 3.1 Search Strategy -- 4 Related Literature Survey in This Area -- 5 Discussion -- 6 Conclusion and Future Scope -- References -- NFT HUB-The Decentralized Non-Fungible Token Market Place -- 1 Introduction -- 1.1 Non-Fungible Token -- 1.2 Blockchain -- 2 Literature Survey -- 3 Proposed System -- 3.1 Blockchain -- 3.2 Ethereum Blockchain -- 3.3 Properties of Blockchain -- 3.4 Generic Elements of the Blockchain -- 3.5 Secure Hash Algorithm -- 4 Results and Discussion -- 4.1 Deployment Confirmation -- 4.2 Metamask Wallet -- 4.3 Minting Page -- 4.4 Transaction Address -- 4.5 Dashboard -- 5 Conclusion -- 5.1 Future Scope of the Project -- References -- Hashgraph: A Decentralized Security Approach Based on Blockchain with NFT Transactions -- 1 Introduction -- 1.1 Optimized Blockchain Server -- 2 Literature Survey -- 2.1 Survey Report: On Token Service -- 2.2 Survey Report: On Main-Net Access -- 2.3 Survey Report: Remove Centralized Control: Hala Systems -- 3 Proposed System -- 3.1 Non-Fungible Transaction -- 3.2 Blockchain Methodology -- 3.3 Gossip Protocol -- 3.4 Hashgraph -- 3.5 Consensus Algorithm [In Gossip Protocol] -- 3.6 HCS Implementation -- 4 Performance Evaluation -- 4.1 Transaction Cost Denomination of Our Network -- 4.2 Transaction Cost of Network on Blockchain NFT -- 5 Result and Discussion -- 6 Conclusion -- References -- Med Card: An Innovative Way to Keep Your Medical Records Handy and Safe -- 1 Introduction -- 1.1 What is Blockchain?.
1.2 What is Machine Learning? -- 2 Literature Review -- 3 Technologies Used -- 3.1 Blockchain in Health Care -- 3.2 Machine Learning in Health Care -- 4 Proposed Idea -- 4.1 What is a Med Card? -- 4.2 Workflow -- 5 Conclusion and Future Scope -- References -- Delta Operator-Based Modelling and Control of High Power Induction Motor Using Novel Chaotic Gorilla Troop Optimizer -- 1 Introduction -- 2 Formulation of the Problem -- 3 New Chaotic Gorilla Troop Optimizer -- 4 Simulation Ouput and Their Analysis -- 5 Conclusions -- References -- An Enhanced Optimize Outlier Detection Using Different Machine Learning Classifier -- 1 Introduction -- 2 Outlier Detection -- 3 Literature Review -- 4 Proposed Methodology -- 5 Simulation Result -- 6 Conclusion -- References -- Prediction of Disease Diagnosis for Smart Healthcare Systems Using Machine Learning Algorithm -- 1 Introduction -- 2 Heart Disease -- 3 Diabetes Mellitus -- 4 Proposed Methodology -- 5 Simulation Results -- 6 Conclusion -- References -- Optimization Accuracy on an Intelligent Approach to Detect Fake News on Twitter Using LSTM Neural Network -- 1 Introduction -- 2 Road Map of Methodology -- 2.1 Data Collection and Filtration -- 2.2 Pre-processing -- 2.3 Feature Extraction -- 2.4 Feature Selection -- 2.5 Classification -- 3 Proposed Methodology -- 4 Simulation Results -- 5 Conclusion -- References -- Advance Computing -- Mining User Interest Using Bayesian-PMF and Markov Chain Monte Carlo for Personalised Recommendation Systems -- 1 Introduction -- 2 Related Work and Background -- 3 Experimental Methodology -- 3.1 Probabilistic Matrix Factorization (PMF) -- 3.2 Bayesian-PMF with MCMC -- 3.3 Sparsity Reduction -- 3.4 Performance Measure -- 4 Proposed Architecture -- 5 Experimental Outcomes and Investigation -- 5.1 Dataset Description -- 6 Conclusion and Future Work -- References. Big Data and Its Role in Cybersecurity -- 1 Introduction -- 2 Security Using Big Data -- 2.1 Use of Big Data Analytic (BDA) as Defense Tool -- 2.2 Cybersecurity and Machine Learning -- 3 Cybersecurity Approaches in Big Data -- 4 Research Trends and Challenges -- 4.1 Use of Big Data in Defense -- 4.2 Laws Regulating to Big Data -- 4.3 Distribution of Data for Storage -- 4.4 Security Technique Scalability in the Big Data -- 5 Conclusions -- References -- QR Code-Based Digital Payment System Using Visual Cryptography -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Implementation -- 4.1 Encryption -- 4.2 AES Algorithm -- 4.3 Decryption -- 5 Result -- 6 Conclusion -- References -- A Study of Different Approaches of Offloading for Mobile Cloud Computing -- 1 Introduction -- 2 Research Method -- 3 Offloading for Mobile Cloud Computing -- 4 Applications Suitable for Offloading in MCC -- 5 Different Offloading Approaches -- 6 Conclusion -- References -- Use of Machine Learning Models for Analyzing the Accuracy of Predicting the Cancerous Diseases -- 1 Introduction -- 2 Related Work -- 3 Machine Learning Models -- 3.1 Logistic Regression -- 3.2 Decision Tree -- 3.3 Random Forest Classifier -- 4 Experiment -- 4.1 Experiment Environment -- 4.2 Breast Cancer Dataset -- 5 Experimental Results -- 6 Conclusion -- References -- Predict Employee Promotion Using Supervised Classification Approaches -- 1 Introduction -- 2 Related Works -- 3 Implementation -- 3.1 Proposed Model -- 3.2 Data Collection -- 3.3 Data Analysis -- 3.4 Engineered Functions -- 3.5 Train-Test-Split -- 3.6 Model Building -- 4 Performance Evaluation -- 5 Conclusion -- References -- Smart Grid Analytics-Analyzing and Identifying Power Distribution Losses with the Help of Efficient Data Regression Algorithms -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Results. 5 Conclusion -- References -- Load Balancing on Cloud Using Genetic Algorithms -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Work -- 4 Results -- 5 Conclusions and Future Work -- References -- Network Security and Telecommunication -- Hybrid Feature Selection Approach to Classify IoT Network Traffic for Intrusion Detection System -- 1 Introduction -- 2 Literature Survey -- 3 Research Gaps -- 4 Proposed Work -- 4.1 Proposed Methodology -- 5 Experimental Results -- 5.1 Dataset Description -- 5.2 System Configuration -- 5.3 Model Parameters -- 5.4 Performance Parameters -- 6 Conclusion -- References -- A Deep Learning-Based Framework for Analyzing Stress Factors Among Working Women -- 1 Introduction -- 2 Literature Survey -- 3 Stress Factors (Stressors) -- 3.1 Deep Learning: Background -- 3.2 Deep Learning-Based Framework for Early Stress Detection -- 4 Results and Discussion -- 5 Conclusion -- References -- Automatic Question Generation -- 1 Introduction -- 2 Literature Survey -- 3 Proposed System -- 4 Methodology -- 5 Conclusion and Future Scope -- References -- Automatic Construction of Named Entity Corpus for Adverse Drug Reaction Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Biomedical Named Entity Recognition -- 2.2 Datasets for Biomedical Entity Recognition -- 3 Proposed Work -- 3.1 ADR Corpus Overview -- 3.2 Corpus Construction Steps -- 4 Results -- 4.1 Contributions -- 4.2 Statistics -- 5 Discussion -- References -- A Space Efficient Metadata Structure for Ranking Subset Sums -- 1 Introduction -- 1.1 Problem Formulation -- 1.2 Past Work -- 1.3 Our Contribution -- 2 Outline of Our Technique -- 3 Generation of Top-kk Subsets -- 3.1 The Metadata Structure upper GG -- 3.2 Construction of Metadata Structure upper GG -- 3.3 Query Answering with Heap -- 3.4 Modified Metadata Structure upper GG -- 3.5 The Final Structure of upper GG. 3.6 Generation of Top-kk Subsets by upper GG on Demand -- 3.7 Getting Rid of the Bit String -- 4 Experimental Results -- 4.1 Analysis of the Results -- 5 Conclusion -- References -- Brain Tumour Detection Using Machine Learning -- 1 Introduction -- 2 Literature Survey -- 3 Methodology and Materials -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- Implementation of a Smart Patient Health Tracking and Monitoring System Based on IoT and Wireless Technology -- 1 Overview -- 2 Block Diagram -- 3 Methodology and Results -- 3.1 Database Tracking and Maintenance -- 4 Conclusion and Future Scope -- References -- Diabetes Disease Prediction Using KNN -- 1 Introduction -- 2 Literature Survey -- 2.1 How is Data Science Revolutionizing the Healthcare Industry? -- 2.2 Role of Data Science in Health Care -- 2.3 Diabetes Detection Using Data Science -- 2.4 Flow of the Methodology Used for Diabetes Detection -- 3 Proposed System -- 4 Results -- 4.1 Experimental Results -- 4.2 Visualization of Obtained Results -- 5 Conclusion -- 6 Future Scope -- References -- Review: Application of Internet of Things (IoT) for Vehicle Simulation System -- 1 Introduction -- 2 Application -- 3 Basic Concepts of IoT -- 4 Simulation Concept -- 5 Simulation-Based Internet of Things -- 6 Application of Simulated IoT for System Used in Vehicle -- 7 Car Simulation Structure and Principle -- 8 Embedding Security Features in a Vehicle Using IoT -- 9 Conclusion and Future Scope -- References -- Data Science and Data Analytics -- Time Series Analysis and Forecast Accuracy Comparison of Models Using RMSE-Artificial Neural Networks -- 1 Introduction -- 2 Related Research -- 3 Neural Networks Models in Time Series -- 3.1 Feed-Forward Neural Network Model -- 3.2 Time-Lagged Neural Networks -- 3.3 Seasonal Artificial Neural Networks -- 3.4 Long Short-Term Memory (LSTM). 4 Time Series Analysis. |
| Record Nr. | UNINA-9910728933703321 |
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Bhattacharya Abhishek
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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