DeepfakeDetection

KO | EN

gitlite — search

#python #llm #golang #android #linux #docker #macos #nodejs #java #windows #vue #awesome-list #pytorch #mcp

DeepfakeDetection

(★ 10)

Deepfake Image Detection with Lightweight Models

DeepfakeDetection Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Deepfake Image Detection with EfficientNet, FasterViT, and EfficientFormerV2

![Python](https://img.shields.io/badge/python-3.11+-blue.svg)
![License](https://img.shields.io/github/license/thourihan/DeepfakeDetection.svg)
![GitHub release](https://img.shields.io/github/v/release/thourihan/DeepfakeDetection.svg)
![Last Commit](https://img.shields.io/github/last-commit/thourihan/DeepfakeDetection.svg)
![Stars](https://img.shields.io/github/stars/thourihan/DeepfakeDetection?style=social)
![Framework](https://img.shields.io/badge/framework-PyTorch-red.svg)

## Project Overview
This project detects deepfake images using modern CNN/ViT-family backbones. We use EfficientNet, FasterViT, and EfficientFormerV2-S1 to balance speed and accuracy, and to diversify architectures to reduce brittleness to dataset-specific artifacts. Models are integrated via `timm`, with reproducible training/evaluation and Grad-CAM visualizations.

Check out the research paper [here](https://docs.google.com/document/d/1Duh0sKFxBPB-_-t1U8HRtR3py7OVKb715McTbgyHh7Q/edit?usp=sharing)!

## Features
- **Multiple Backbones (via timm):** EfficientNet, FasterViT, and EfficientFormerV2-S1 for complementary accuracy/latency trade-offs.
- **Gradio Interface:** Simple web UI for uploading images and viewing model predictions.
- **Grad-CAM Visualization:** Heatmaps highlighting the regions driving model decisions (available across models).
- **Training & Evaluation Scripts:** Scripts for training and evaluation with Grad-CAM outputs.

## Sample Predictions and Heatmap Visualizations
Here are a couple of examples of the model's output, along with heatmap visualizations for each.

### Deepfake Detection Example
**Mark Zuckerberg Deepfake**  
Predictions:  
EfficientNet: fake (62.11% confidence)  
FasterViT: fake (97.33% confidence)

<table>
  <tr>
    <td>
      <p><strong>Input Image</strong></p>
      <img src="docs/images/mark-zuckerberg-deepfake.webp" alt="Mark Zuckerberg Deepfake" width="361" height="224"/>
    </td>
    <td>
      <p><strong>Heatmap Visualization</strong></p>
      <img src="docs/images/mark-zuckerberg-deepfake-heatmap.png" alt="Heatmap of Mark Zuckerberg Deepfake" width="224" height="224"/>
    </td>
  </tr>
</table>

### Real Image Detection Example
**Donald Trump Real**  
Predictions:  
EfficientNet: real (97.83% confidence)  
FasterViT: real (98.55% confidence)

<table>
  <tr>
    <td>
      <p><strong>Input Image</strong></p>
      <img src="docs/images/donald-trump-real.jpg" alt="Donald Trump Real" width="301" height="224"/>
    </td>
    <td>
      <p><strong>Heatmap Visualization</strong></p>
      <img src="docs/images/donald-trump-real-heatmap.png" alt="Heatmap of Donald Trump Real" width="224" height="224"/>
    </td>
  </tr>
</table>

## Installation
To set up the project, follow these steps:
1. **Clone the repository.**
2. **Install dependencies:** We use Python 3.12+  
   Install with:
   ```bash
   pip install -r requirements.txt
   ```

## Orchestrating runs (training & inference)

There are three main entrypoints, all routed through `orchestrator.py` and driven by YAML configs under `config/`:

- **`train.py`**: trains the models listed under `models:` in `config/train.yaml` (or a custom config).
- **`inference.py`**: runs batch evaluation using `config/inference.yaml`, logging metrics, plots, and confusion matrices.
- **`web_ui.py`**: launches a browser-based Gradio UI for single-image inference and Grad-CAM overlays, using the same config and `selection` as the orchestrator.

### Commands

1. **Train models listed in `config/train.yaml`:**
   ```bash
   python train.py
   ```

2. **Evaluate models defined in `config/inference.yaml`:**
   ```bash
   python inference.py
   ```

3. **Run the interactive web UI (Grad-CAM enabled):**
   ```bash
   python web_ui.py --config config/inference.yaml
   ```
   - Honors the YAML’s `models` and `selection` so the UI stays in sync with orchestrator runs.
   - Uses the per-model transforms and weight paths from the config (relative to the current working directory or absolute paths).
   - Exports CAM composites to `outputs/cam_exports/`.

Pass `--config` to any script to point at an alternate YAML while keeping the same structure.

Each run directory contains `checkpoints/` (latest & best checkpoints), `logs/` (console outputs), and `plots/` (confusion matrix and ROC curve when labels are available). The setup targets frame-level deepfake vs. real classification but works for multiclass `ImageFolder` datasets as well.

### Per-model transform toggles

Every transform in the training and evaluation pipelines can be toggled on or off per backbone. Add a `transforms:` block under each `models.<name>` entry in the YAML config and enable the transforms you need for training/inference for each model.

DeepfakeDetection

DeepfakeDetection Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

DeepfakeDetection

DeepfakeDetection Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README