AML-Project

Deep fake Image Detection

1. Data Collection Gather a dataset containing real and deepfake videos/images. Use public datasets like DFDC (DeepFake Detection Challenge), FaceForensics++, Celeb-DF, or DeepFake-TIMIT. Ensure diversity in the dataset (various lighting conditions, angles, and resolutions).
2. Preprocessing Extract frames from videos. Perform face detection using MTCNN or OpenCV. Normalize and resize images for consistency.
3. Feature Extraction Identify facial inconsistencies (artifacts, unnatural blinking, distortions). Use image/video analysis techniques like frequency domain analysis, texture patterns, and motion inconsistencies.
4. Model Selection Choose a CNN-based model (EfficientNet, Xception, ResNet) for image-based detection. Use RNN or LSTM with CNN if working with videos (to analyze frame sequences). Consider Vision Transformers (ViTs) or GAN detection models for better accuracy.
5. Training the Model Train on GPU with a well-balanced dataset. Use data augmentation (flipping, rotation, noise addition) to prevent overfitting. Choose a binary classification approach (real vs. fake).
6. Evaluation Use Precision, Recall, F1-score, and AUC-ROC for performance metrics. Validate the model with unseen deepfake samples to check robustness.
7. Deployment Create a web or mobile interface for users to upload videos/images for detection. Use Flask, FastAPI, or Streamlit for deployment.
8. Challenges to Address Handling high-resolution and low-resolution deepfakes. Ensuring the model generalizes well to unseen datasets. Detecting audio deepfakes if required.

#what all i should learn

1. Python & Libraries Python Basics (if not already familiar) Libraries: NumPy, Pandas, OpenCV, Matplotlib, Seaborn 🔹 2. Computer Vision & Image Processing OpenCV (face detection, image manipulation) Dlib (facial landmarks, deepfake artifacts) Mediapipe (for face tracking and analysis) 🔹 3. Deep Learning Basics Neural Networks (ANN, CNN, RNN basics) Activation functions (ReLU, Sigmoid, Softmax) Loss functions (Cross-Entropy, Binary Classification Losses) 🔹 4. Deepfake-Specific Techniques CNN Architectures (ResNet, EfficientNet, Xception – commonly used for deepfake detection) Autoencoders & GANs (to understand how deepfakes are generated) Vision Transformers (ViTs) (optional but useful for advanced detection) Motion Analysis (for video-based deepfake detection) 🔹 5. Model Training & Evaluation Transfer Learning (using pre-trained models like Xception, EfficientNet) Hyperparameter Tuning (learning rate, batch size, epochs) Evaluation Metrics (Accuracy, Precision, Recall, F1-score, AUC-ROC) 🔹 6. Working with Deepfake Datasets DFDC (DeepFake Detection Challenge) FaceForensics++ Celeb-DF, DeepFake-TIMIT Dataset Preprocessing (frame extraction, resizing, normalization) 🔹 7. Model Deployment & API Development Flask / FastAPI (to create an API for deepfake detection) Streamlit / Gradio (for simple web-based model UI) TensorFlow.js / ONNX (if deploying for mobile/web apps)