Modeling the EXIF-Image correlation for image manipulation detection
EXchangeable Image File format (EXIF) is a metadata header containing shot-related camera settings such as aperture, exposure time, ISO speed etc. These settings can affect the photo content in many ways. In this paper, we investigate the underlying EXIF-Image correlation and propose a novel model, which correlates image statistical noise features with several commonly used EXIF features. By formulating each EXIF feature as a weighted combination of different image statistical noise features, we first select a compact image statistical noise feature set using sequential floating forward selection. The underlying correlation as a set of regression weights is then solved using a least squares solution. When applying our learned correlation to detect image manipulation, we achieve average test accuracies of 94.6%, 94.1% and 94.9% in three different cameras to detect the presence of common image brightness and contrast adjustment.