🧠 Image Steganography using Neural Networks and Autoencoders

This MATLAB project demonstrates image steganography using convolutional autoencoders, where one image (the secret) is invisibly embedded into another (the cover). A dedicated extraction network is used to recover the secret image later with high fidelity.

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📌 Project Highlights

• MATLAB implementation with Deep Learning Toolbox
• Embedding and Extraction CNN-based networks
• Trains on 1000 pairs of images resized to 256×256
• Stego images generated and saved to disk
• Secret images reconstructed with high perceptual quality
• PSNR and SSIM used for evaluation
• Includes code, report, and presentation

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📐 Block Diagrams

🔒 Embedding Network

🔓 Extraction Network

🧠 Architecture Overview

• Embedding Network
  Inputs: cover and secret images
  Layers: parallel conv layers → concatenation → more convs → stego image

• Extraction Network
  Input: stego image
  Layers: conv stack → relu activations → reconstruct secret

• Input size: 256×256×3

• Optimizer: Adam, learning rate: 0.001

• Epochs: 10 (can be tuned)

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🚀 How to Run

1. Launch MATLAB or MATLAB Online
2. Clone/download this repo
3. Edit code/steganography_pipeline.m to set correct paths for:
   • cover_dataset/
   • secret_dataset/
   • stego_images_output/
4. Run the script
5. Output includes:
   • Saved stego images
   • Extracted secret images
   • PSNR and SSIM averages

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📊 Sample Results

Metric	Value
PSNR	~30.5 dB
SSIM	~0.93

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🗂 Repository Structure

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📄 License

This project is licensed under the MIT License.

Full license text is available in the LICENSE file

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👤 Author

Niranjan Meti
Deep learning + embedded systems enthusiast
📫 LinkedIn
🌐 GitHub

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