Steganography is the art of hidden storage or transmission of information. For instance, a message embedded in a harmlessly looking image can be used to communicate without attracting unwanted attention. The reverse process, the detection of the presence or absence of steganographic manipulation in an information-carrying medium is called steganalysis.

The goal of this project is the development of new and more powerful steganalysis methods for images, especially tools for the so-called universal steganalysis, i.e., steganalysis without knowing the specific method of steganographic manipulation. Universal steganalysis requires the statistical characterisation of unmanipulated images. In our case, we will build on our previous experience in image modeling (e.g., our kernel PCA or Wiener series image model) to describe the typical properties of unmanipulated images. A significant deviation from these properties in an image could be used to detect a steganographic manipulation.