Forensic Detection Using Bit-Planes Slicing of Median Filtering Image

For the detection of median filtering (MF) forensics, this paper proposes the feature vector extracted from the bit-planes slicing of the forged image. The assembled feature vector is trained in a support vector machine (SVM) classifier for the MF detection (MED) of the forged images. The performance of the proposed MFD scheme is measured with several types of forged images: unaltered, Gaussian filtering (3 x 3), averaging filtering (3 x 3), downscaling (0.9), upscaling (1.1), and post-frame-up, respectively, in a block size 32 x 32 and 64 x 64 pixels. Subsequently, in experimental items, a classification ratio, Area Under the Curve (AUC), P-TP at P-FP = 0.01, and Pe (a minimum average decision error) are estimated. The result in terms of AUC shows that the estimation of the proposed MFD scheme is graded as 'Excellent (A)'.