Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
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
Differentiation of discrete multidimensional signals
IEEE Transactions on Image Processing
Detecting filtered cloning in digital images
Proceedings of the 9th workshop on Multimedia & security
Hash-based identification of sparse image tampering
IEEE Transactions on Image Processing
Tamper hiding: defeating image forensics
IH'07 Proceedings of the 9th international conference on Information hiding
Imaging sensor noise as digital X-ray for revealing forgeries
IH'07 Proceedings of the 9th international conference on Information hiding
A novel PCA-based authentication watermarking scheme with superior localization and security
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
A bibliography on blind methods for identifying image forgery
Image Communication
Digital image forensics: a booklet for beginners
Multimedia Tools and Applications
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
Detection of doctored images using correlations of PSF
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
| Hi-index | 0.00 |
A recent case of scientific fraud, involving manipulated images in a high-profile scientific publication, has sent shock- waves through the scientific community. By some measures, however, this case is not isolated - in at least one journal, it is estimated that as many as 20% of accepted manuscripts contain figures with inappropriate manipulations, and 1% with fraudulent manipulations. Several scientific editors are considering putting safeguards in place to help reduce these numbers. While sensible policy and awareness are certainly important, there is likely to be a need for computational techniques that automatically detect common forms of tampering. We describe three such techniques for detecting traces of tampering in scientific images. Specifically, image segmentation techniques are employed to detect image deletion, "healing", and duplication.