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https://github.com/mawg0ud/faceguard
A framework to detect & localize facial image manipulation.
https://github.com/mawg0ud/faceguard
artificial-intelligence cnn cybersecurity deeplearning digitalforensics facialrecognition imagemanipulation machinelearning
Last synced: 18 days ago
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A framework to detect & localize facial image manipulation.
- Host: GitHub
- URL: https://github.com/mawg0ud/faceguard
- Owner: mawg0ud
- Created: 2024-10-27T09:11:38.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-22T10:06:46.000Z (about 1 month ago)
- Last Synced: 2024-12-22T11:19:53.595Z (about 1 month ago)
- Topics: artificial-intelligence, cnn, cybersecurity, deeplearning, digitalforensics, facialrecognition, imagemanipulation, machinelearning
- Language: Python
- Homepage:
- Size: 393 KB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
Awesome Lists containing this project
README
# FaceGuard:
# Localization of Facial Images Manipulation in Digital Forensics
This project is an implementation of the paper *[Localization of Facial Images Manipulation in Digital Forensics via Convolutional Neural Networks](https://link.springer.com/chapter/10.1007/978-981-33-6129-4_22)*. This project, named **FaceGuard**, aims to detect and localize manipulated facial images, a critical task in digital forensics, using advanced deep learning techniques. This repository includes the full code to train, evaluate, and experiment with a Y-shaped Auto-Encoder model for detecting and segmenting manipulated areas within facial images.
---
## Table of Contents
1. [Introduction](#introduction)
2. [Project Structure](#project-structure)
3. [Features](#features)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Results](#results)
7. [Citation](#citation)
8. [License](#license)
---
## Introduction
**FaceGuard** implements a model for detecting facial image manipulations, based on a Y-shaped Auto-Encoder architecture that simultaneously handles classification, segmentation, and reconstruction tasks. The model is evaluated on the **FaceForensics++** dataset and includes functionality for:
- Training the Y-shaped Auto-Encoder to detect manipulated images and localize regions of manipulation.
- Experimenting with various configurations and hyperparameters.
- Evaluating the model on both seen and unseen manipulation types.
### Paper Abstract
> The remarkable phenomenon of computer-generated spoofing images poses a societal threat and is an important concern of digital forensics. In this work, we propose a deep learning model to localize manipulated regions in facial images via a Y-shaped Auto-Encoder architecture, which simultaneously handles classification, segmentation, and reconstruction tasks. We evaluate our approach using datasets such as FaceForensics++ and achieve promising results in terms of both accuracy and localization capabilities.
---
## Project Structure
The repository is organized as follows:
```
.
├── src/ # Source code for model, data loading, and utilities
│ ├── data_preprocessing/ # Data loading, augmentation, and preprocessing scripts
│ ├── models/ # Y-shaped Auto-Encoder and related model code
│ ├── utils/ # Utilities for logging, configuration, and plotting
│ ├── train.py # Main training script
│ └── test.py # Main testing script
├── experiments/ # Scripts for running different experimental configurations
├── notebooks/ # Jupyter notebooks for EDA and model visualization
├── datasets/ # Dataset downloading and preparation scripts
├── configs/ # YAML configuration files for different experiments
├── results/ # Saved models, evaluation metrics, and plots
├── logs/ # Training and testing log files
└── requirements.txt # Project dependencies
```
---
## Features
- **End-to-End Model Implementation**: Code for training, evaluating, and testing the Y-shaped Auto-Encoder model.
- **Flexible Experimentation**: Configuration files for running different experiments and hyperparameter tuning.
- **Robust Data Processing Pipeline**: Includes augmentation, data splitting, and preprocessing for facial images.
- **Logging and Tracking**: Detailed logging setup for tracking training progress, metrics, and model checkpoints.
---
## Installation
### 1. Clone the Repository
```bash
git clone https://github.com/mawg0ud/FaceGuard.git
cd FaceGuard
```
### 2. Install Dependencies
Ensure you have Python 3.7+ and `pip` installed. Install the dependencies:
```bash
pip install -r requirements.txt
```
---
## Usage
### 1. Training
To train the Y-shaped Auto-Encoder model, use the following command. This command uses a configuration file for model and training settings.
```bash
python src/train.py --config configs/experiment1_baseline.yaml
```
### 2. Testing
To evaluate the trained model on the test set, run:
```bash
python src/test.py --config configs/experiment3_unseen_attacks.yaml
```
### 3. Running Experiments
You can run different experiments with configurations provided in the `configs/` directory. For example, to tune hyperparameters, use:
```bash
python experiments/experiment2_tune_hyperparameters.py
```
### 4. Visualization and Analysis
Explore the results using the provided Jupyter notebooks in the `notebooks/` folder. Run:
```bash
jupyter notebook notebooks/1_data_exploration.ipynb
```
---
## Results
The following results were obtained using the Y-shaped Auto-Encoder model on the FaceForensics++ dataset:
| Metric | Seen Attacks | Unseen Attacks |
|--------------|----------------|----------------|
| Accuracy | 93.5% | 85.3% |
| EER | 4.7% | 7.8% |
| Segmentation | 90.1% | 82.0% |
Visualization of segmentation masks produced by the model is provided in the [notebooks/3_test_on_samples.ipynb](notebooks/3_test_on_samples.ipynb) notebook. The model effectively localizes manipulated regions and achieves high accuracy on both seen and unseen manipulation types.
---
## Citation
If you use this code or the model in your work, please cite the following paper:
> Mawgoud, A. A., Albusuny, A., Abu-Talleb, A., & Tawfik, B. S. (2021). Localization of Facial Images Manipulation in Digital Forensics via Convolutional Neural Networks. In *Enabling Machine Learning Applications in Data Science* (pp. 313-324). Springer. DOI: [10.1007/978-981-33-6129-4_22](https://link.springer.com/chapter/10.1007/978-981-33-6129-4_22)
```bibtex
@inproceedings{Mawgoud2021Localization,
title={Localization of Facial Images Manipulation in Digital Forensics via Convolutional Neural Networks},
author={Ahmed A. Mawgoud and Amir Albusuny and Amr Abu-Talleb and Benbella S. Tawfik},
booktitle={Enabling Machine Learning Applications in Data Science},
pages={313--324},
year={2021},
publisher={Springer},
doi={10.1007/978-981-33-6129-4_22}
}
```
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---