Raw tool identification through detected demosaicing regularity

Authors:
Hong Cao;Alex C. Kot
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 3
Cited 0

Eigenfeature Regularization and Extraction in Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital camera identification from sensor pattern noise

IEEE Transactions on Information Forensics and Security
Nonintrusive Component Forensics of Visual Sensors Using Output Images

IEEE Transactions on Information Forensics and Security

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.