Yedrouj-Net: An efficient CNN for spatial steganalysis
This work addresses steganalysis for security applications, but it is incremental as it builds on recent CNN approaches by cleverly fusing existing components.
The authors tackled the problem of detecting hidden messages in images (spatial steganalysis) by proposing Yedrouj-Net, a CNN that outperforms state-of-the-art methods in terms of error probability, as evaluated against S-UNIWARD and WOW embedding algorithms.
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.