Photo-montage and counterfeit images are not a recent phenomenon. As soon as images have been used within a political or economical context, the reality and the authenticity of recorded scenes become a legitimate question. The emergence of digital data did not really modify that basic context.
The modification of digital images is now a fact, in particular in the domain of cyber-criminality. Modifications can be naive (slight modifications done in order to remove spots present in a facial pictures), controversial (removal of visual object's defaults on a online commercial website) or may have strong societal impacts (improbable meeting between two important politician persons).
This project takes place in the domain of image forensics. The main goal is to certify if an image is a clean or a doctored image. The associated decision process must be as reliable as possible because the digital proof of falsification is really credible only if the method of detection returns very limited number of errors.
In a first step, it is proposed to develop some methods to detect malicious modifications of digital images from two complementary approaches, a first approach based on the modelisation of the digital image acquisition process and a second approach based on machine learning. Considering that the two approaches are scientifically complementary, it will be then proposed to fuse them in order to form a unique detector of digital image integrity.