Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Image steganalysis with binary similarity measures
EURASIP Journal on Applied Signal Processing
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Source Camera Identification for Low Resolution Heavily Compressed Images
ICCSA '08 Proceedings of the 2008 International Conference on Computational Sciences and Its Applications
Accurate detection of demosaicing regularity for digital image forensics
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Source camera identification using enhanced sensor pattern noise
IEEE Transactions on Information Forensics and Security
A bibliography on blind methods for identifying image forgery
Image Communication
Detecting doctored JPEG images via DCT coefficient analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Blind Identification of Source Cell-Phone Model
IEEE Transactions on Information Forensics and Security
Determining Image Origin and Integrity Using Sensor Noise
IEEE Transactions on Information Forensics and Security
Digital Image Forensics via Intrinsic Fingerprints
IEEE Transactions on Information Forensics and Security
Nonintrusive Component Forensics of Visual Sensors Using Output Images
IEEE Transactions on Information Forensics and Security
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Fast proliferation of mobile cameras and the deteriorating trust on digital images have created needs in determining the integrity of photos captured by mobile devices. As tampering often creates some inconsistencies, we propose in this paper a novel framework to statistically detect the image tampering inconsistency using accurately detected demosaicing weights features. By first cropping four non-overlapping blocks, each from one of the four quadrants in the mobile photo, we extract a set of demosaicing weights features from each block based on a partial derivative correlation model. Through regularizing the eigenspectrum of the within-photo covariance matrix and performing eigenfeature transformation, we further derive a compact set of eigen demosaicing weights features, which are sensitive to image signal mixing from different photo sources. A metric is then proposed to quantify the inconsistency based on the eigen weights features among the blocks cropped from different regions of the mobile photo. Through comparison, we show our eigen weights features perform better than the eigen features extracted from several other conventional sets of statistical forensics features in detecting the presence of tampering. Experimentally, our method shows a good confidence in tampering detection especially when one of the four cropped blocks is from a different camera model or brand with different demosaicing process.