Image steganalysis and steganography in the spatial domain

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. En esta tesis, proponemos diferentes técnicas novedosas para detectar información oculta (estegoanálisis) y para ocultar información (esteganografı́a). Estas técnicas se presentan como una colección de cinco contribuciones, que comparten un problema común. La primera contribución presenta tres métodos diferentes para detectar información oculta usando técnicas de desplazamiento de histograma, algunas de las cuales son ataques dirigidos a esquemas concretos, mientras que las otras son más genéricas. En la segunda contribución, en el área del aprendizaje automático aplicado al estegoanálisis, presentamos un nuevo extractor de caracterı́sticas para detectar información oculta en el dominio espacial, que se puede usar como submodelo adicional en el framework rich models, y que supera la precisión obtenida por el método del estado del arte subtractive pixel adjacency matrix (SPAM) usando un número inferior de caracterı́sticas. En el mismo contexto, la tercera contribución es un algoritmo esteganográfico que explota la debilidad de algunos