CVTStego-Net: a convolutional vision transformer architecture for spatial image steganalysis

The principal investigations in image steganalysis in the spatial domain have concentrated on convolutionalneural network (CNN) designs. However, existing CNNs increase the local receptive field of steganographicnoise without considering global steganographic noise. This study introduces CVTStego-Net, a convolutionalvision transformer for spatial domain image steganalysis that merges the strengths of convolutions and the advantages of attention mechanisms to capture both local and global dependencies. CVTStego-Net is composed of three stages: preprocessing stage, noise extraction, and analysis stage, and classification stage. The preprocessing stage involves a bifurcation with trainable and untrainable 30 SRM (Spatial Rich Models) filters to enhance steganographic noise. The noise extraction and analysis stage combines the SE-Block (Squeeze-and-Excitation) with residual operations to increase the sensitivity to steganographic noise and suppressing the influence of redundant information, and the classification stage combines SE-Block with a convolutional vision transformer to connect the local and global spatial relationships of the steganographic noise. This work enhanced the classification accuracies for steganographic algorithms compared to YEDROUDJ-Net, SR-Net,ZHU-Net, GBRAS-Net, and SNMC-Net. Specifically, the accuracy of CVTStego-Net for WOW at 0.2 bpp was 86.58%, and 0.4 bpp was 93.80%. Moreover, for S-UNIWARD at 0.2 and 0.4 bpp, the accuracies were 80.70% and 90.45%, respectively. For MiPOD at 0.2 and 0.4 bpp, the accuracies were 74.70% and 81.48%, respectively.For HILL at 0.2 and 0.4 bpp, the accuracies were 76.70% and 85.80%, respectively, and for HUGO at 0.2 and 0.4 bpp, the accuracies were 78.20% and 86.98%, respectively, using test data from the BOSSbase 1.01. The results demonstrate that convolutional vision transformers can classify steganographic images in the spatial domain.

Folders

• transformer_1.py This file contains the model used in the various training experiments carried out for the research. To use it, the database must be varied and the hyperparameters adjusted to obtain the desired results, while keeping the same code.

• SRM_Kernels1.npy This file contains the weights of the 30 SRM high-pass filters used for model training.

Requirements

This repository requires the following libraries and frameworks:

• TensorFlow 2.10.0
• scikit-learn
• numPy
• OpenCV
• Matplotlib
• os
• scikit-image

This repository was developed in the Python3 (3.9.12) programming language.

Databases

The data set used to reproduce the results can be downloaded from: Here. Images taken from: BOSS competition, BOWS2 and ALASKA2 .

Authors

Universidad Autonoma de Manizales (https://www.autonoma.edu.co/)

• Mario Alejandro Bravo-Ortiz
• Esteban Mercado-Ruiz
• Juan Pablo Villa-Pulgarin
• Harold Brayan Arteaga-Arteaga
• Reinel Tabares-Soto

References

[1]