Yedrouj-Net: An efficient CNN for spatial steganalysis

Mehdi Yedroudj 1 Frédéric Comby 1 Marc Chaumont 1, 2
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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-of-the-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 filter-bank 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.
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Communication dans un congrès
ICASSP: International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada. 43rd IEEE International Conference on Acoustics, Speech and Signal Processing, 2018, 〈https://2018.ieeeicassp.org〉
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Dernière modification le : jeudi 24 mai 2018 - 15:59:23
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Mehdi Yedroudj, Frédéric Comby, Marc Chaumont. Yedrouj-Net: An efficient CNN for spatial steganalysis. ICASSP: International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada. 43rd IEEE International Conference on Acoustics, Speech and Signal Processing, 2018, 〈https://2018.ieeeicassp.org〉. 〈lirmm-01717550〉

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