models/README.md

🔍 Deepfake Image Detection

Binary Classifier — Real vs Fake

This project implements a high-performance deepfake detection system using the InceptionResnetV1 architecture (pretrained on VGGFace2). It is designed to distinguish between real human faces and AI-generated/deepfake images, supporting modern AI sources like Midjourney, Stable Diffusion, and DALL-E.

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🚀 Key Features

• Modern AI Support: Custom data loaders for Midjourney, SD, DALL-E, and Flux images.
• HuggingFace Integration: Seamlessly merges the riandika/AI-vs-Deepfake-vs-Real-Resized-Aug dataset.
• GPU Acceleration: Fully optimized for NVIDIA GPUs (RTX 40 series) with CUDA 12.1.
• Pause & Resume: Training state is automatically saved to checkpoint.pt, allowing you to interrupt and resume anytime.
• Diffusion-Aware Augmentation: Specialized transforms (JPEG compression, Gaussian Blur) to catch subtle AI artifacts.
• Rich Visualization: Automated plotting of training loss and validation accuracy.

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📂 Project Structure

File/Folder	Description
Deepfake_Detection.ipynb	Main Jupyter Notebook for training and inference.
Deepfake_Detection.py	Python script version of the detection pipeline.
requirements.txt	List of Python dependencies.
checkpoint.pt	Saved training state (epoch, optimizer, model weights).
models/	Directory where final trained models are saved.
training_curves.png	Visualization of the latest training run.

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🛠️ Prerequisites

• Python: 3.12 (Recommended via Conda)
• Conda: For environment management (Miniconda or Anaconda)
• GPU: NVIDIA GPU with CUDA 12.1 support (Standard training runs on CPU but is much slower)

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⚙️ Installation & Setup

1. Clone the Project

# Navigate to your project folder
cd C:\Users\Downloads\deepfake_main

2. Create a Virtual Environment (Conda)

It is highly recommended to use a dedicated environment to avoid package conflicts.

conda create -n deepfake_env python=3.12 -y
conda activate deepfake_env

3. Install Standard Dependencies

pip install -r requirements.txt

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⚡ Dedicated GPU Installation (NVIDIA)

To leverage your RTX 40-series GPU (e.g., RTX 4050), follow these specific steps to install PyTorch with CUDA 12.1 support.

1. Install PyTorch with CUDA 12.1

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

2. Verify GPU Recognition

Run the following Python snippet in your terminal:

import torch
print(f"CUDA Available: {torch.cuda.is_available()}")
print(f"GPU Name: {torch.cuda.get_device_name(0)}")

If it returns True, your GPU is ready.

3. Troubleshooting

• Ensure you have the latest NVIDIA Drivers installed from nvidia.com.
• If you encounter a DLL load failed error, try installing the Microsoft Visual C++ Redistributable.

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🎯 Usage Guide

1. Configure Paths

Open Deepfake_Detection.py or the Notebook and adjust the CONFIG dictionary:

CONFIG = {
    'train_dir' : r'C:\path\to\your\train_data',
    'valid_dir' : r'C:\path\to\your\valid_data',
    'test_dir'  : r'C:\path\to\your\test_data',
    # ... other settings
}

2. Run Training

Using Jupyter Notebook: Simply open Deepfake_Detection.ipynb in VS Code or Jupyter Lab and run all cells.

Using Terminal:

python Deepfake_Detection.py

3. Pause & Resume

• To Pause: Simply interrupt the execution (Ctrl+C in terminal or Stop in Notebook).
• To Resume: Re-run the script or training cell. It will detect checkpoint.pt and pick up where it left off.

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🔎 Single Image Inference

To test an image, use the provided predict_image function in the notebook:

result = predict_image("path/to/test_image.jpg", model, device)
print(f"Verdict: {result['label']} ({result['confidence']*100:.2f}%)")

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📊 Results

After training, check training_curves.png to see how the model's loss and accuracy improved over time.

Final models are stored in the /models directory with a .pt extension.