Linked e-resources
Details
Table of Contents
Intro
Committee
Message from General Chairs
Message from Program Chairs
Preface
Contents
About the Editors
Verifiable Delay Function Based on Non-linear Hybrid Cellular Automata
1 Introduction
1.1 Our Contributions
1.2 Organization of the Paper
2 Related Work
2.1 Review of Verifiable Delay Functions
2.2 Cryptography Based on Non-linear CA
3 Preliminaries
3.1 Notation
3.2 Verifiable Delay Function
3.3 Cellular Automata
4 VDF Based on Cellular Automata
4.1 The Setup(1,T) Algorithm
4.2 The Eval(pp,x) Algorithm
5 Conclusion
References
MILP Modeling of S-box: Divide and Merge Approach
1 Introduction
2 MILP Modeling of S-boxes
2.1 Generating Linear Inequalities of DDT
2.2 Inequality Minimization Using Impossible Transitions
2.3 Existing Improvements in Inequality Minimization
2.4 Inequality Minimization: Divide and Merge Approach
3 Experimental Results: 4-Bit and 5-Bit S-boxes
3.1 Divide and Merge Approach: k=2
3.2 Divide and Merge Approach: k=3
4 Conclusion
References
A Relation Between Properties of S-box and Linear Inequalities of DDT
1 Introduction
2 Non-linear S-boxes
2.1 Properties of S-box
3 Methods for Construction and Minimization of Linear Inequalities of DDT
3.1 H-representation of Convex Hull
3.2 Boolean Logic Minimization Tools-Logic Friday, MILES
3.3 Limitation of Convex Hull Approach and Logic Friday Tool
4 Results
4.1 Experiments
4.2 Relation Between Boomerang Uniformity and Number of Linear Inequalities
5 Conclusion
References
Damage Level Estimation of Rubble-Mound Breakwaters Using Deep Artificial Neural Network
1 Introduction
2 Deep Artificial Neural Network
3 Dataset
4 Development of the Deep ANN-Based Damage Level Estimation Model
5 Results and Discussion
5.1 Performance Analysis and Model Accuracy
5.2 Comparison of the Proposed Deep ANN Model with the Existing ANN-Based Damage Level Estimation Model
6 Conclusion
References
Facial Image Manipulation Detection Using Cellular Automata and Transfer Learning
1 Introduction
2 Digital Image Manipulation Detection
3 Results
3.1 Experimental Data
3.2 Application Previews
3.3 Performance Metrics of the Proposed Solution
3.4 Comparison with Other Works
4 Conclusion
Committee
Message from General Chairs
Message from Program Chairs
Preface
Contents
About the Editors
Verifiable Delay Function Based on Non-linear Hybrid Cellular Automata
1 Introduction
1.1 Our Contributions
1.2 Organization of the Paper
2 Related Work
2.1 Review of Verifiable Delay Functions
2.2 Cryptography Based on Non-linear CA
3 Preliminaries
3.1 Notation
3.2 Verifiable Delay Function
3.3 Cellular Automata
4 VDF Based on Cellular Automata
4.1 The Setup(1,T) Algorithm
4.2 The Eval(pp,x) Algorithm
5 Conclusion
References
MILP Modeling of S-box: Divide and Merge Approach
1 Introduction
2 MILP Modeling of S-boxes
2.1 Generating Linear Inequalities of DDT
2.2 Inequality Minimization Using Impossible Transitions
2.3 Existing Improvements in Inequality Minimization
2.4 Inequality Minimization: Divide and Merge Approach
3 Experimental Results: 4-Bit and 5-Bit S-boxes
3.1 Divide and Merge Approach: k=2
3.2 Divide and Merge Approach: k=3
4 Conclusion
References
A Relation Between Properties of S-box and Linear Inequalities of DDT
1 Introduction
2 Non-linear S-boxes
2.1 Properties of S-box
3 Methods for Construction and Minimization of Linear Inequalities of DDT
3.1 H-representation of Convex Hull
3.2 Boolean Logic Minimization Tools-Logic Friday, MILES
3.3 Limitation of Convex Hull Approach and Logic Friday Tool
4 Results
4.1 Experiments
4.2 Relation Between Boomerang Uniformity and Number of Linear Inequalities
5 Conclusion
References
Damage Level Estimation of Rubble-Mound Breakwaters Using Deep Artificial Neural Network
1 Introduction
2 Deep Artificial Neural Network
3 Dataset
4 Development of the Deep ANN-Based Damage Level Estimation Model
5 Results and Discussion
5.1 Performance Analysis and Model Accuracy
5.2 Comparison of the Proposed Deep ANN Model with the Existing ANN-Based Damage Level Estimation Model
6 Conclusion
References
Facial Image Manipulation Detection Using Cellular Automata and Transfer Learning
1 Introduction
2 Digital Image Manipulation Detection
3 Results
3.1 Experimental Data
3.2 Application Previews
3.3 Performance Metrics of the Proposed Solution
3.4 Comparison with Other Works
4 Conclusion