Statistical Modeling and Detection for Digital Image Forensics
Troyes University of Technology
The twenty-first century witnesses the digital revolution that allows digital media to become ubiquitous. They play a more and more important role in our everyday life. Similarly, sophisticated image editing software has been more accessible, resulting in the fact that falsified images are appearing with a growing frequency and sophistication. The credibility and trustworthiness of digital images have been eroded. To restore the trust to digital images, the field of digital image forensics was born. This thesis is part of the field of digital image forensics. Two important problems are addressed: image origin identification and hidden data detection. These problems are cast into the framework of hypothesis testing theory. The approach proposes to design a statistical test that allows us to guarantee a prescribed false alarm probability. In order to achieve a high detection performance, it is proposed to exploit statistical properties of natural images by modeling the main steps of image processing pipeline of a digital camera. The methodology throughout this manuscript consists of studying an optimal test given by the Likelihood Ratio Test in the ideal context where all model parameters are known in advance. When the model parameters are unknown, a method is proposed for parameter estimation in order to design a Generalized Likelihood Ratio Test whose statistical performances are analytically established. Numerical experiments on simulated and real images highlight the relevance of the proposed approach.