RAW tool identification through detected demosaicing regularity
RAW tools are PC software tools that develop the RAWs, i.e. the camera sensor data, into full-color photos. In this paper, we propose to study the internal processing characteristics of these RAW tools using 3 heterogeneous sets of demosaicing features. Through feature-level fusion, normalization and an Eigen-space regularization technique, we derive a compact set of discriminant features. Experimentally, we find that the compact feature set can be used to accurately distinguish 40 RAW-tool classes. A dissimilarity study also shows that the cropped image blocks from different RAW-tool or positional classes have a great deal of dissimilarity in our extracted demosaicing features.