Yedrouj-Net: An efficient CNN for spatial steganalysis

TL;DR

Yedrouj-Net is a novel CNN architecture designed for spatial steganalysis, combining innovative components like a filterbank, truncation activation, and extensive training data to outperform existing methods.

Contribution

The paper introduces Yedrouj-Net, a CNN that surpasses state-of-the-art steganalysis performance through a unique fusion of architectural elements and training strategies.

Findings

Yedrouj-Net outperforms existing steganalysis methods in error probability.

The use of a filterbank and truncation activation improves detection accuracy.

Augmented training data enhances the CNN's learning capability.

Abstract

For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN. Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.