README.md

Deep Residual Network for Steganalysis of Digital Images (SRNet model) Pytorch Implementation:

This is an unofficial implementation of paper: "Deep Residual Network for Steganalysis of Digital Images" The model can be tested using the file test.py The tensorflow code of the same can be found at: http://dde.binghamton.edu/download/feature_extractors/

The test accuracy reported in the paper is 89.77%. My implementation achieved 89.43% on S-Uniward 0.4bpp.

The model is trained and tested on Tesla V-100-DGX with 32GB GPU.

SRNet architecture

Datasets:

You can find cover images here: BOSSbase_1.01.zip

Steganography algorithms here: SUniward, WOW, and MiPOD.

Create corresponding stego images for each cover image with steganography algorithm of your choice. Make sure to change random seed for each image to get random key dataset.