The Effect of Median Filtering on Edge Estimation and Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Nonintrusive Component Forensics of Visual Sensors Using Output Images
IEEE Transactions on Information Forensics and Security
Hiding Traces of Resampling in Digital Images
IEEE Transactions on Information Forensics and Security
JPEG compression history estimation for color images
IEEE Transactions on Image Processing
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In digital image forensics, there is an increasing need for the development of techniques to identify general content-preserving operations, such as resampling, compression, contrast enhancement and median filtering (MF). As a contribution towards this goal, we present, in this paper, a new blind forensic scheme for MF detection in images. The proposed method is based on the observation that, compared with original and linear filtered images, median filtered images exhibit distinct intrinsic traces around edges, e.g. neighborhood correlation, noise suppression and good edge preservation. Such MF intrinsic fingerprints are characterized as the Edge Based Prediction Matrix (EBPM), which contains the estimated prediction coefficients of neighborhood prediction among different edge regions in images. By incorporating the support vector machine (SVM), the MF detector is developed based on EBPM. Extensive simulations are carried out, which demonstrates the superior performance of the proposed scheme in terms of effectiveness and robustness.