Yedroudj-Net: An Efficient CNN for Spatial Steganalysis
| Autor: | Mehdi Yedroudj, Marc Chaumont, Frédéric Comby |
|---|---|
| Přispěvatelé: | Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Nîmes (UNIMES) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Steganalysis
Normalization (statistics) Computer science business.industry Deep learning Activation function Feature extraction 020207 software engineering Pattern recognition Convolutional Neural Network 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Filter bank Convolutional neural network [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] Deep Learning [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) |
| Zdroj: | 43rd International Conference on Acoustics, Speech and Signal Processing ICASSP: International Conference on Acoustics, Speech and Signal Processing ICASSP: International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada. pp.2092-2096, ⟨10.1109/ICASSP.2018.8461438⟩ ICASSP |
| DOI: | 10.1109/ICASSP.2018.8461438⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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