A comprehensive neural network-based steganography toolkit for hiding data across multiple media types.
While encryption scrambles data to make it unreadable, steganography hides the very existence of the data itself.
Stego AI leverages deep learning to embed messages within ordinary-looking media files in ways that are difficult to detect, resistant to manipulation, and capable of hiding large amounts of data.
Neural network-powered data hiding
with imperceptible changes
Uses neural networks instead of traditional bit manipulation for superior quality and capacity.
Unified framework for hiding data across 5 different media types with consistent API.
Adversarially trained models minimize statistical artifacts and evade common steganalysis techniques.
Built-in Reed-Solomon codes ensure message integrity even with some media degradation.
Dynamically adjusts embedding strength based on media characteristics for optimal hiding.
Train your own models with custom data and architectures for specialized use cases.
Built-in tools to evaluate capacity, detect artifacts, and benchmark resistance to steganalysis.
Seamlessly combines with cryptography for multilayered security with encrypted payloads.
from stegoai import get_model
# Load an image steganography model
model = get_model('image', architecture='dense')
# Hide a message
model.encode('cover.png', 'stego.png',
'This is a secret message!')
# Extract a hidden message
message = model.decode('stego.png')
print(message) # 'This is a secret message!'Clone the repository and install from source:
git clone https://github.com/azhrdev/stego-ai.gitcd stego-aipip install -e .For development with all dependencies: pip install -e ".[dev,all]"
Comprehensive guides, tutorials, and API documentation are available in the repository.
View DocumentationFor sensitive data, always use encryption before applying steganography. While Stego AI provides strong steganography capabilities, it's best used as part of a comprehensive security approach. The security of steganographic systems fundamentally depends on keeping the method itself secret.