Accurate and Efficient Image Forgery Detection Using Lateral Chromatic Aberration

In copy-and-paste image forgeries, where image content is copied from one image and pasted into another, inconsistencies in an imaging feature called lateral chromatic aberration (LCA) are intrinsically introduced. In this paper, we propose a new methodology to detect forged image regions that is based on detecting localized LCA inconsistencies. To do this, we propose a statistical model that captures the inconsistency between global and local estimates of LCA. We then use this model to pose forgery detection as a hypothesis testing problem and derive a detection statistic, which we show is optimal when certain conditions are met. To test its detection efficacy, we conduct a series of experiments that demonstrate our proposed methodology significantly outperforms prior art and addresses deficiencies of previous research. Additionally, we propose a new and efficient LCA estimation algorithm. To accomplish this we adapt a block matching algorithm, called diamond search, which efficiently measures the LCA in a localized region. We experimentally show that our proposed estimation algorithm reduces estimation time by two orders of magnitude without introducing additional estimation error.