Deep Fake Detection Using CNN

Week 1: Introduction to Deep Learning and Deepfakes

Topics:

• Overview of deep learning, neural networks, and their applications
• Introduction to deepfakes: history, types, and impact on society
• How AI generates deepfakes using GANs (Generative Adversarial Networks)

Homework/Practice:

• Watch introductory videos on deep learning
• Read articles on GANs and deepfake ethics

Week 2: Convolutional Neural Networks (CNN) Basics

Topics:

• Introduction to CNNs: architecture, convolution, pooling, and fully connected layers
• Role of CNNs in image recognition and classification
• Overview of how CNNs are applied for deepfake detection

Homework/Practice:

• Implement a simple CNN on image classification (e.g., MNIST dataset)

Week 3: Introduction to the Deepfake Detection Project

Topics:

• Overview of the project: detecting deepfakes using deep learning models
• Introduction to datasets (Kaggle’s Deepfake Detection Challenge and StyleGAN-generated images)
• Project objectives and comparison of two models (CNN from scratch vs. Xception)

Homework/Practice:

• Explore Kaggle datasets and become familiar with data formats and classes

Week 4: Preprocessing the Deepfake Datasets

Topics:

• Data preprocessing: resizing, normalization, noise removal, and augmentation
• Importance of preparing data for deep learning models

Homework/Practice:

• Preprocess the Kaggle datasets using Python (e.g., resizing images to 224x224, normalizing values)

Week 5: Building a CNN from Scratch

Topics:

• CNN architecture from scratch: layer design, filter sizes, activations (ReLU), pooling
• How to design and train a CNN for deepfake detection

Homework/Practice:

• Implement a CNN from scratch using the Kaggle dataset for deepfake detection

Week 6: Transfer Learning and Xception Model

Topics:

• Introduction to transfer learning: using pre-trained models for specific tasks
• Xception model architecture and its role in deepfake detection

Homework/Practice:

• Implement transfer learning using Xception for the same dataset

Week 7: Training the Models and Hyperparameter Tuning

Topics:

• Training CNN and Xception models: learning rates, epochs, batch size
• Hyperparameter tuning for optimizing performance

Homework/Practice:

• Train both models on the deepfake dataset and compare their performance

Week 8: Evaluating the Models (Accuracy, Precision, Recall, F1-Score)

Topics:

• Evaluation metrics for deepfake detection models: accuracy, precision, recall, F1-score
• Understanding the confusion matrix and its significance in deepfake detection

Homework/Practice:

• Evaluate the performance of both models and create confusion matrices

Week 9: Model Comparison and Insights

Topics:

• Comparative analysis of CNN from scratch vs. Xception model
• Discussion on performance trade-offs and model efficiency

Homework/Practice:

• Analyze and report the model comparison

Week 10: Improving the Deepfake Detection Models

Topics:

• Handling imbalanced datasets: oversampling, undersampling, class weights
• Advanced techniques: ensemble learning, regularization (dropout, L2 regularization)

Homework/Practice:

• Implement methods to handle imbalanced data and improve model performance

Week 11: Deployment of Deepfake Detection Models

Topics:

• Exporting models for deployment
• Introduction to deployment on cloud platforms (optional)

Homework/Practice:

• Prepare the trained model for deployment

Week 12: Conclusion and Future Directions

Topics:

• Summarizing the project: successes, challenges, and areas for improvement
• Future directions for research in deepfake detection and combating misinformation

Homework/Practice:

• Finalize the project report and reflect on possible improvements for deepfake detection models