Demosaicing to Detect Demosaicing and Image Forgeries

The trustfulness of images is a critical concern. Digital photographs can no longer be assumed to be truthful; indeed, digital image editing tools can easily and convincingly alter the semantic content of an image.Being able to analyse an image to check for forgeries becomes of the utmost importance in many domains, from police investigations to fact-checking and journalism.We propose here to analyse traces left by the camera during demosaicing, one of the first steps of image formation. When an object is added or displaced on an image, the demosaicing traces can be disrupted, leaving forgery clues.In order to detect these inconsistencies, we explore the possibilities offered by double demosaicing. Computing the demosaicing residual of an image with different demosaicing algorithms and patterns enables one to find image regions with inconsistent demosaicing traces.We render the method fully automatic by a simple a contrario scheme computing forgery detection thresholds with statistical guarantees on the number of false alarms. See github.com/qbammey/ddem for the code and an online demo.