Abstract
Attention to tampering by median filtering (MF) has recently increased in digital image forensics. For the MF detection (MFD), this paper presents a feature vector that is extracted from two kinds of variations between the neighboring line pairs: the row and column directions. Of these variations in the proposed method, one is defined by a gradient difference of the intensity values between the neighboring line pairs, and the other is defined by a coefficient difference of the Fourier transform (FT) between the neighboring line pairs. Subsequently, the constructed 19-dimensional feature vector is composed of these two parts. One is the extracted 9-dimensional from the space domain of an image and the other is the 10-dimensional from the frequency domain of an image. The feature vector is trained in a support vector machine classifier for MFD in the altered images. As a result, in the measured performances of the experimental items, the area under the receiver operating characteristic curve (AUC, ROC) by the sensitivity (PTP: the true positive rate) and 1-specificity (PFP: the false-positive rate) are above 0.985 and the classification ratios are also above 0.979. Pe (a minimal average decision error) ranges from 0 to 0.024, and PTP at PFP=0.01 ranges from 0.965 to 0.996. It is confirmed that the grade evaluation of the proposed variation-based MF detection method is rated as “Excellent (A)” by AUC is above 0.9.
Highlights
In the image alterations for the forgeries, the tampering uses compression, filtering, averaging, rotating, mosaic editing, and updownscaling
4.2 Experimental Results The proposed method compared with existing works: the MFR AR2 and the median filtering forensics (MFF) methods.[3]
This paper proposed a variation-based MF detection (MFD) method, the constructed feature vector that was composed of two kinds of variations from the space and the frequency domain in an image
Summary
In the image alterations for the forgeries, the tampering uses compression, filtering, averaging, rotating, mosaic editing, and updownscaling. MF detection could classify the altered images with MF.[1] the prior studies[2,3,4,5] emphasized that the MF detector becomes a significant forensic tool for the recovery of the processing history of a forgery image. To extract the 10 features for MF detection (MFD), Kang et al.[2] obtained autoregressive (AR) coefficients as feature vectors via an AR model to analyze the median filter residual (MFR AR), which is the difference between the values of the original image and those of the median-filtered image. The authors analyzed an image’s MFR AR; it is able to suppress image content that may interfere with MFD
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