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Questions and Answers

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?

  • A deep fake video of Indian actress Rashmika Mandanna showing her in a compromising situation.
  • Videos falsely attributing harmful speeches or actions to Prime Minister Narendra Modi.
  • A deepfake video of Mark Zuckerberg making controversial statements about controlling people's data.
  • All of the above (correct)

What are the main types of machine learning algorithms used in this project to detect deepfakes?

  • Convolutional Neural Networks (CNNs)
  • Generative Adversarial Networks (GANs)
  • Both A and B (correct)
  • None of the above

The NVIDIA Flickr dataset has shown superior performance compared to the Yonsei dataset.

<p>True (A)</p> Signup and view all the answers

What are some of the key challenges faced during the development of this deepfake detection system?

<p>All of the above (H)</p> Signup and view all the answers

What are the primary evaluation metrics used to assess the performance of the deepfake detection system?

<p>The primary evaluation metrics include accuracy, precision, recall, F1-score, confusion matrix, ROC-AUC, and AP score.</p> Signup and view all the answers

What are some of the key areas for future development of this deepfake detection system?

<p>Future development areas include: hybrid model development, GAN integration, real-time inference, explainability and trust, and broader applications.</p> Signup and view all the answers

Flashcards

Deepfakes

Highly realistic fake images or videos created to manipulate real content.

Generative Adversarial Networks (GANs)

AI models used to create realistic fake content, like deepfakes.

Fake Image Detection

The process of identifying manipulated or artificial images.

Convolutional Neural Network (CNN)

A type of neural network specialized for image analysis.

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Machine Learning (ML)

Computer algorithms that improve automatically with experience.

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Artificial Intelligence (AI)

The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

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Dataset

Collection of images used to train and evaluate the fake detection model.

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Training Data

Images that teach the model to differentiate real from fake images.

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Testing Data

Images used to assess the model's performance and ability to generalize.

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Validation Data

Images used to adjust model parameters during training.

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Model Accuracy

The percentage of correctly classified images.

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Pre-processing

Preparing images for training, including resizing and normalization.

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ResNet

A deep convolutional neural network architecture used for image recognition tasks.

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EfficientNet

A CNN architecture known for its balance of accuracy and efficiency in deepfake detection tasks.

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NVIDIA GPU

A graphics processing unit (GPU) by NVIDIA used to speed up operations.

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Python libraries

Software tools used for data processing and analysis.

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TensorFlow/Keras

Popular machine learning frameworks used for CNN implementation

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OpenCV

Library used for image processing tasks in projects

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Google Colab

A platform offering GPUs for enhanced deep learning task performance.

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Jupyter Notebook

Interactive coding environment enabling visual exploration and experimentations

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CUDA

Compute Unified Device Architecture; a parallel computing platform and programming model

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Data Augmentation

Enhancing dataset diversity and reducing over fitting by adding variations to images.

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Image Normalization

Rescaling pixel values to a standard range.

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Data Splitting

Dividing images into training, validation, and testing sets.

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Parameter Optimization

Adjusting model settings to maximize accuracy and efficiency.

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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

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Fake Image Detection Project Report PDF

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