GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis

Advances in Deep Learning (DL) have provided alternative approaches to various complex problems, including the domain of spatial image steganalysis using Convolutional Neural Networks (CNN). Several CNN architectures have been developed in recent years, which have improved the detection accuracy of...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.14340-14350
Main Authors: Reinel, Tabares-Soto, Brayan, Arteaga-Arteaga Harold, Alejandro, Bravo-Ortiz Mario, Alejandro, Mora-Rubio, Daniel, Arias-Garzon, Alejandro, Alzate-Grisales Jesus, Buenaventura, Burbano-Jacome Alejandro, Simon, Orozco-Arias, Gustavo, Isaza, Raul, Ramos-Pollan
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Artificial neural networks
Computer architecture
Convolution
Convolutional neural network
Convolutional neural networks
deep learning
Feature extraction
Filter banks
GBRAS-Net
Machine learning
Neural networks
Payloads
steganalysis
Steganography
Task analysis
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Summary:Advances in Deep Learning (DL) have provided alternative approaches to various complex problems, including the domain of spatial image steganalysis using Convolutional Neural Networks (CNN). Several CNN architectures have been developed in recent years, which have improved the detection accuracy of steganographic images. This work presents a novel CNN architecture which involves a preprocessing stage using filter banks to enhance steganographic noise, a feature extraction stage using depthwise and separable convolutional layers, and skip connections. Performance was evaluated using the BOSSbase 1.01 and BOWS 2 datasets with different experimental setups, including adaptive steganographic algorithms, namely WOW, S-UNIWARD, MiPOD, HILL and HUGO. Our results outperformed works published in the last few years in every experimental setting. This work improves classification accuracies on all algorithms and bits per pixel (bpp), reaching 80.3% on WOW with 0.2 bpp and 89.8% on WOW with 0.4 bpp, 73.6% and 87.1% on S-UNIWARD (0.2 and 0.4 bpp respectively), 68.3% and 81.4% on MiPOD (0.2 and 0.4 bpp), 68.5% and 81.9% on HILL (0.2 and 0.4 bpp), 74.6% and 84.5% on HUGO (0.2 and 0.4 bpp), using BOSSbase 1.01 test data.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3052494