StegoFace is a deep learning-based steganography model designed to enhance ID image security by embedding hidden authentication data within facial images. The system ensures tamper detection while preserving image quality and integrity, making it a reliable solution for photo substitution attack prevention.
- 🛡️ Deep CNN-Based Steganography for secure message embedding.
- 🔍 Binary Error-Correcting Codes (BECC) for robustness against noise and compression.
- 🔄 Autoencoder-Decoder Framework for high-precision encoding and decoding.
- 🎯 Recurrent Proposal Network (RPN) for accurate facial region detection.
- ⚡ Real-time verification for ID security applications.
- 🐍 Python
- 🤖 TensorFlow/Keras (Deep Learning)
- 🖼️ OpenCV (Image Processing)
- 📊 NumPy & Pandas (Data Handling)
- 📈 Matplotlib & Seaborn (Visualization)
📦 StegoFace
┣ 📂 dataset
┣ 📂 models
┣ 📂 preprocessing
┣ 📂 encoder_decoder
┣ 📂 utilss
┣ 📜 requirements.txt
┣ 📜 README.md
┗ 📜 main.py
1. Clone the Repository
git clone https://github.com/yourusername/StegoFace.git
cd StegoFace- Install Dependencies Ensure you have all necessary libraries installed by running:
pip install -r requirements.txt- Run the Program Execute the main script to start the system:
python main.pyPreprocessing Module: Enhances image quality and extracts facial features.
Encoding Phase: The autoencoder securely embeds authentication data into ID images.
Decoding Phase: The auto decoder retrieves hidden messages for verification.
Verification: If the extracted message is intact, the ID is considered valid; otherwise, tampering is detected.
Sai Dhanush V.R
M. Mukunda
Surya J
This project is licensed under the MIT License.
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