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Title: Image steganalysis and steganography in the spatial domain
Author: Lerch-Hostalot, Daniel  
Director: Megías Jiménez, David
Keywords: steganography
machine learning
image processing
Issue Date: 23-Mar-2017
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: In this dissertation, we propose different novel techniques both to detect hidden information (steganalysis) and to hide information (steganography). These techniques are presented in the form of a collection of five contributions, but sharing a common research problem. The first contribution presents three different methods to detect histogram shifting data hiding techniques, some of which are targeted attacks to specific schemes, whereas others are more general. As a second contribution, in the area of machine learning steganalysis, we present a novel feature extractor to detect information hidden in the spatial domain, which can be used as an additional submodel in the rich models framework, and which outperforms the accuracy of the state-of-the-art steganalysis by subtractive pixel adjacency matrix (SPAM) with fewer features. In the same context, the third contribution is a steganographic algorithm that exploits the weakness of some submodels to deal with high dimensional data (which typically use a threshold to overcome the dimensionality problem). As a fourth contribution, we present a new framework for unsupervised steganalysis with accuracy higher than the supervised methods in the state of the art, while bypassing the cover source mismatch (CSM) problem. Finally, as a fifth contribution, we present a novel approach to address the CSM problem based on the set of machine learning techniques known as manifold alignment.
Language: English
Appears in Collections:Tesis doctorals

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