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
Improving steganalysis by fusion techniques: a case study with image steganography
Transactions on Data Hiding and Multimedia Security I
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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In this paper, we propose a novel framework to statistically measure the correlation inconsistency in mobile images for tamper detection. By first sampling a number of blocks at different image locations, we extract a set of derivative weights as features from each block using partial derivative correlation models. Through regularizing the within-image covariance eigenspectrum and performing eigenfeature transformation, we derive a compact set of eigen weights, which are sensitive to image signal mixing from different source models. A metric is then proposed to quantify the inconsistency among the sampled blocks at different image locations. Through comparison, our eigen weights features show better performance than the eigenfeatures from several other types of forensics features in detecting the presence of tampering. Experimentally, our method shows good tamper detection performance especially when a small percentage of sampled blocks are from a different camera model or brand with different demosaicing processing.