Steganography Neural Network Model Card
Model Overview
Task: Image Steganography (Message Embedding and Extraction)
Architecture Type: Encoder-Decoder Neural Network
Primary Use Case: Embedding and recovering hidden messages in images
Technical Specifications
- Parameters: 980,548
- Model Size: 3.74 MB
- Precision: torch.float32
- FLOPs: 1,954,176
- Input Resolution: 512 × 512 pixels
- Framework: PyTorch
Architecture Details
Encoder Network
- Input: 4 channels (RGB + message), 512×512px
- Output: 3 channels (RGB stego image), 512×512px
- Key Components:
- Initial Conv (4→64 channels)
- Backbone with SE blocks and dilated convolutions
- Residual connections
- Final weighted combination (0.9 × original + 0.1 × encoded)
Decoder Network
- Input: 3 channels (stego image), 512×512px
- Output: 1 channel (recovered message), 512×512px
- Key Components:
- Feature extraction (3→64→128 channels)
- SE blocks and residual connections
- Message extraction pathway
Training Details
- Hardware: GTX 1080 GPU
- Epochs: 600
- Optimizer: AdamW (lr=0.001, weight_decay=0.01)
- Scheduler: Cosine Annealing (min_lr=1e-6)
- Loss Functions:
- Image Loss: 0.95×MSE + 0.05×(1-SSIM)
- Message Loss: MSE
- Combined with dynamic alpha weighting
Key Features
- Group Normalization for batch-size independence
- SiLU activation functions throughout
- Squeeze-and-Excitation blocks for channel attention
- Dilated convolutions in encoder
- Skip connections for detail preservation
Performance Characteristics
- Maintains visual image quality while embedding messages
- Optimized for both image fidelity and message recovery
- Lightweight architecture (<1M parameters)
Limitations and Biases
- Fixed input resolution of 512×512 pixels
Technical Requirements
- PyTorch environment
- GPU recommended for optimal performance
- Standard deep learning dependencies
- Sufficient memory for 3.74 MB model
Citation and Contact
- Model source and citation information not provided
- Contact information for maintainers not specified