Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Doctored Images Using Camera Response Normality and Consistency
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Exposing digital forgeries in scientific images
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
Identifying shifted double JPEG compression artifacts for non-intrusive digital image forensics
CVM'12 Proceedings of the First international conference on Computational Visual Media
A Survey of Digital Forensic Techniques for Digital Libraries
International Journal of Digital Library Systems
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In this paper, we describe a new forensic tool for revealing digitally altered images by detecting the presence of photo-response nonuniformity noise (PRNU) in small regions. This method assumes that either the camera that took the image is available to the analyst or at least some other nontampered images taken by the camera are available. Forgery detection using the PRNU involves two steps - estimation of the PRNU from non-tampered images and its detection in individual image regions. From a simplified model of the sensor output, we design optimal PRNU estimators and detectors. Binary hypothesis testing is used to determine which regions are forged. The method is tested on forged images coming from a variety of digital cameras and with different JPEG quality factors. The approximate probability of falsely identifying a forged region in a non-forged image is estimated by running the algorithm on a large number of non-forged images.