This repo implements a blind steganography system using a Convolutional Neural Network (CNN). It allows us to hide a grayscale SECRET inside a full-color COVER image, producing a STEGO IMAGE, that looks almost identical to the COVER.
Steganography hides the very existence of the message in a image. In digital imaging, this involves modifying the pixel values of a cover image to embed secret data without creating visible artifacts.
The system uses an Encoder-Decoder framework:
Encoder: Takes a 3-channel RGB Cover image and a 1-channel Grayscale Secret image (Total 4 channels).
Decoder: Takes only the 3-channel Stego image and learns to reconstruct the 1-channel Secret image.
Invisibility: The Stego image must look like the Cover.
Recovery: The Extracted Secret must look like the original Secret.
We use weighted MSE loss to balance equality:
A higher β (e.g., 0.85) ensures the Stego image remains visually perfect.
The train_images of STL - 10: Image Recognition Dataset has been used. It has ~7000 images.