Towards Learned Color Representations for Image Splicing Detection

Abstract

The detection of images that are spliced from multiple sources is one important goal of image forensics. Several methods have been proposed for this task, but particularly since the rise of social media, it is an ongoing challenge to devise forensic approaches that are highly robust to common processing operations such as strong JPEG recompression and downsampling.In this work, we make a first step towards a novel type of cue for image splicing, which is based on the color formation of an image. We make the assumption that the color formation is a joint result of the camera hardware, the software settings, and the depicted scene, and as such can be used to locate spliced patches that originally stem from different images. To this end, we train a two-stage classifier on the full set of colors from a Macbeth color chart, and compare two patches for their color consistency. Our preliminary results on a challenging dataset on downsampled data of identical scenes indicate that the color distribution can be a useful forensic tool that is highly resistant to JPEG compression.