https://github.com/shakthirekak11/deepfake-detection-using-graph-convolutional-networks
🤖 Deepfake detection using Graph Convolutional Networks (GCNs) to analyze facial landmark graphs. Combines computer vision, graph-based learning, and facial geometry for robust media forensics.
https://github.com/shakthirekak11/deepfake-detection-using-graph-convolutional-networks
cnn deep-learning deepfake-detection face-detection facial-landmarks graph-convolutional-networks keras-tensorflow opencv-python python pytorch transfer-learning video-classification
Last synced: 20 days ago
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🤖 Deepfake detection using Graph Convolutional Networks (GCNs) to analyze facial landmark graphs. Combines computer vision, graph-based learning, and facial geometry for robust media forensics.
- Host: GitHub
- URL: https://github.com/shakthirekak11/deepfake-detection-using-graph-convolutional-networks
- Owner: Shakthirekak11
- Created: 2025-04-26T08:22:51.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-05-07T10:12:02.000Z (24 days ago)
- Last Synced: 2025-05-12T08:07:04.271Z (20 days ago)
- Topics: cnn, deep-learning, deepfake-detection, face-detection, facial-landmarks, graph-convolutional-networks, keras-tensorflow, opencv-python, python, pytorch, transfer-learning, video-classification
- Language: Python
- Homepage:
- Size: 1.68 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## 🔍 Deepfake Detection Using Graph Convolutional Networks (GCNs)
In this project, we tackle the growing threat of deepfakes—AI-generated fake videos that can spread misinformation and violate personal privacy—by moving beyond traditional CNN-based detection methods. Instead, we leverage **Graph Convolutional Networks (GCNs)** to capture the **spatial and geometric relationships between facial landmarks**, providing a more nuanced understanding of facial structures.
### 🚀 What We Did:
* ✅ **Built a deepfake detection pipeline** that transforms video frames into facial landmark graphs.
* 🧠 **Applied GCNs** to model facial dependencies, outperforming CNNs in detecting subtle manipulations.
* 📊 **Achieved over 90% accuracy**, with high precision (92%), recall (89%), and F1-score (90.5%) on the Deepfake Detection Challenge dataset.
* 👁️🗨️ **Visualized facial graphs**, highlighting key regions (eyes, mouth) where manipulation often occurs.
### 🛠️ Skills & Technologies:
* Graph Convolutional Networks (GCNs)
* Facial landmark detection
* Deep learning and computer vision
* Video preprocessing & frame extraction
* Model evaluation (Accuracy, Precision, Recall, F1-Score)
* Python, PyTorch (or TensorFlow, depending on your stack)
This work demonstrates the potential of **graph-based deep learning** in media forensics and opens the door to more robust, interpretable deepfake detection systems.
### 📁 Dataset: Deepfake Detection Challenge (DFDC)
The **DFDC dataset** is a large-scale benchmark developed by Facebook AI in collaboration with industry partners to advance deepfake detection research. It comprises over **100,000 video clips** featuring both real and AI-manipulated content, created using various deepfake generation techniques. The dataset includes:
* **Diverse Subjects**: Videos of **3,426 paid actors**, ensuring a wide range of facial features, expressions, and backgrounds.
* **Varied Manipulations**: Deepfakes generated using multiple face-swapping methods, including GAN-based and non-learned techniques, to simulate real-world scenarios.
* **Balanced Dataset**: A mix of authentic and manipulated videos to train models effectively.
* **Ethical Considerations**: All participants provided consent for their likenesses to be used and altered in the dataset.
This dataset serves as a comprehensive resource for training and evaluating models aimed at detecting deepfake videos. For more details and access to the dataset, visit the [DFDC Kaggle page](https://www.kaggle.com/competitions/deepfake-detection-challenge).
### 🧪 Methodology Overview
Our deepfake detection approach consists of the following key steps:
1. **Frame Extraction**: Extract individual frames from video samples.
2. **Facial Landmark Detection**: Use a landmark detector to identify key facial points (e.g., eyes, nose, mouth).
3. **Graph Construction**: Represent each face as a graph, where nodes are landmarks and edges denote spatial relationships.
4. **GCN Processing**: Pass the facial graphs through a multi-layer Graph Convolutional Network to learn spatial and geometric features.
5. **Classification**: Use the extracted features to classify each frame as **real** or **fake**.
This graph-based method enables the model to understand complex facial structures and detect subtle manipulations typical in deepfake videos.