Podcast
Questions and Answers
What is the primary objective of this project?
What is the primary objective of this project?
The primary objective is to develop an advanced fake image detection system that can distinguish real images from deepfakes with high accuracy.
Which of the following are examples of real-world cases where deepfakes have been used for malicious purposes?
Which of the following are examples of real-world cases where deepfakes have been used for malicious purposes?
What are the main types of machine learning algorithms used in this project to detect deepfakes?
What are the main types of machine learning algorithms used in this project to detect deepfakes?
The NVIDIA Flickr dataset has shown superior performance compared to the Yonsei dataset.
The NVIDIA Flickr dataset has shown superior performance compared to the Yonsei dataset.
Signup and view all the answers
What are some of the key challenges faced during the development of this deepfake detection system?
What are some of the key challenges faced during the development of this deepfake detection system?
Signup and view all the answers
What are the primary evaluation metrics used to assess the performance of the deepfake detection system?
What are the primary evaluation metrics used to assess the performance of the deepfake detection system?
Signup and view all the answers
What are some of the key areas for future development of this deepfake detection system?
What are some of the key areas for future development of this deepfake detection system?
Signup and view all the answers
Study Notes
Project Title and Details
- Project title: Fake Image Detection
- Type of project: Major project report
- Degree: Bachelor of Technology in Computer Science & Engineering
- Department: Computer Science & Engineering and Information Technology
- University: Jaypee University of Information Technology
- Location: Waknaghat, Solan - 173234 (India)
- Submission date: December 2024
- Authors: Jasmeen Kaur, Ritika, Jeetesh Saini
- Supervisor: Dr. Ekta Gandotra
Supervisor's Certificate
- Project title: Fake Image Detection
- Period of supervision: July 2024 to December 2024
- Supervisor's name: Dr. Ekta Gandotra
- Supervisor's title: Associate Professor
- Department: CSE & IT
- Ethical compliance statement: Project work completed under supervision and not submitted elsewhere.
Candidate's Declaration
- Authors: Jasmeen Kaur, Ritika, Jeetesh Saini
- Roll numbers: 211429, 211432, 211436
- Date of declaration: 01/12/2024
- Supervisor name: Dr. Ekta Gandotra
- Verification Statement: The candidates' statements are true to the best of the supervisor's knowledge
Acknowledgement
- Project title: Fake Image Detection
- Gratitude to the institute, supervisor, classmates, friends and family
- Acknowledgement of the researchers and developers in the field of AI, biometric systems and software development.
- The contribution of the project to the cause and inspiration for further advancements.
Table of Contents
- List of certificates and declarations
- Chapter 1: Introduction
- Introduction
- Problem statement
- Objectives
- Motivation
- Organization of the project report
- Chapter 2: Literature Review
- Overview of relevant literature
- Key gaps in literature
- Chapter 3: System Development
- Requirements and analysis
- System requirements (Hardware and Software)
- Key functional requirements
- Project design and architecture
- Overview of project architecture
- Workflow diagram
- Design considerations (modular design, performance, user-friendly interface, error handling)
- Key technologies used (Hardware and Software)
- Data preparation (Data Pipeline)
- Implementation details (data collection, data preprocessing, dataset splitting, model initialization, custom layer design, and model compilation, training configuration, and training)
- Chapter 4: Testing
- Testing strategy (dataset changes, epoch variation, evaluation metrics and hyperparameter tuning)
- Test cases and outcomes
- Chapter 5: Results and Evaluation
- Yonsei dataset
- Nvidia Flickr dataset
- Performance comparison of models
- Chapter 6: Conclusion and Future Scope
- Conclusion
- Future scope (hybrid model development, GAN integration, real-time inference, explainability and trust, and broader applications)
List of Tables
- Overview of relevant literature
- Models Performance comparison on Yonsei Dataset
- Models Performance comparison on NVIDIA Flickr Dataset
List of Figures
- Workflow Diagram
- Project Architecture Diagram
- ResNet50 Model
- XceptionNet Model
- DenseNet121 Model
- VGG16 Model
- Models Performance Comparison on Yonsei Dataset
- Models Performance Comparison on NVIDIA Flickr Dataset
- Confusion Matrix of DenseNet
- Confusion Matrix of ResNet101
- Confusion Matrix of XceptionNet
- WorkFlow Diagram of System
- Project Architecture Diagram
- Resnet50 model
- Xception model
- DenseNet121 model
- VGG16 model
- Models Performance on Yonsei Dataset
- Models Performance on NVIDIA Flickr Dataset
- Confusion matrix of DenseNet
- Confusion matrix of ResNet101
- Confusion matrix of XceptionNet
List of Abbreviations, Symbols, or Nomenclature
- List of abbreviations used in the report
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
