How to augment a small learning set for improving the performances of a CNN-based steganalyzer?
| Autor: | Marc Chaumont, Mehdi Yedroudj, 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: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Convolutional Neural Network 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Machine learning computer.software_genre Convolutional neural network Base augmentation [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] Deep Learning [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 0202 electrical engineering electronic engineering information engineering Steganalysis 021110 strategic defence & security studies business.industry Deep learning Multimedia (cs.MM) Learning set 020201 artificial intelligence & image processing Artificial intelligence business computer Computer Science - Multimedia |
| Zdroj: | Electronic Imaging MWSF: Media Watermarking, Security, and Forensics Electronic Imaging, ingenta CONNECT, 2018, Media Watermarking, Security, and Forensics 2018, 317, pp.317-1-317-7. ⟨10.2352/ISSN.2470-1173.2018.07.MWSF-317⟩ Media Watermarking, Security, and Forensics |
| ISSN: | 2470-1173 |
| DOI: | 10.2352/ISSN.2470-1173.2018.07.MWSF-317⟩ |
| Popis: | Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results. The performances of such networks often rely on the size of their learning database. An obvious preliminary assumption could be considering that "the bigger a database is, the better the results are". However, it appears that cautions have to be taken when increasing the database size if one desire to improve the classification accuracy i.e. enhance the steganalysis efficiency. To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance. What kind of images can be added to the initial learning set? What are the sensitive criteria: the camera models used for acquiring the images, the treatments applied to the images, the cameras proportions in the database, etc? This article continues the work carried out in a previous paper, and explores the ways to improve the performances of CNN. It aims at studying the effects of "base augmentation" on the performance of steganalysis using a CNN. We present the results of this study using various experimental protocols and various databases to define the good practices in base augmentation for steganalysis. EI'2018, in Proceedings of Media Watermarking, Security, and Forensics, Part of IS&T International Symposium on Electronic Imaging, San Francisco, California, USA, 28 Jan. -2 Feb. 2018, 7 pages |
| Databáze: | OpenAIRE |
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