Improvement Feature Vector: Autoregressive Model of Median Filter Residual

Abstract

To detect median filtering forensics, a four-feature ensemble including the median filter residual autoregressive (MFR AR) model, statistical properties, gradient-edge line, and HU invariant moments of an image were used to propose an improved feature vector. The defined novel feature vector was trained on a support vector machine (SVM) classifier for median filtering detection (MFD) of forgery images. The performance of the proposed MFD scheme was measured with several types of images: median filtered (window size: {3 × 3, 5 × 5, composite (3 × 3, 5 × 5)}), JPEG compressed (quality factor: 90) after median filtered, rotated (counterclockwise: 5 •), and noise added (salt-pepper: 5%) which has been re-altered in various ways. Experimental results show high efficiency and performance of the MFD techniques. The area under the curve (AUC) by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed MFD scheme are 0.9 upper with the trained SVM classifier. Thus, the grade evaluation of the proposed scheme is ''Excellent (A).'' INDEX TERMS Forgery image, median filtering detection, digital image forensics, median filter residual, autoregressive model, HU invariant moments, support vector machine.

Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics

“…Kang et al [8] and K.H. Rhee [9] who performed MFD by extracting the AR (Autoregressive) coefficients contained in MFR as feature vectors because of its excellent performance in IFD. Meanwhile, J. Yang et al [10] used the 2D-AR model of MFR, which is fed into the SVM classifier.…”

“…14, which is made of MF (blue color arrow region, w = 3× 3) and JPEG (red color arrow region, QF = 90), which are most commonly used in image transform format, the different transformation region is detected, and the classification ratio is computed. This spliced image has forged in a similar way in [9]. The proposed IFD scheme, which is applied to Fig.…”

“…In the typical image forensics detection (IFD), the relationship between image pixels is statistically processed, or the frequency and spatial domains of an image are analyzed. So whether the extracted feature vector is based on numerical or frequency spatial, a median filter residual (MFR) [7][8][9][10][11] was used a lot. The MFR is mainly used as a pre-processing step of the suspicion image.…”

Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

“…For IFD, feature vectors are classified into two main types. One uses a single property like the autoregressive (AR) model [7,8], and the other one has a combination of various properties [2,9]. In [2], Yuan invented the median filtering forensics (MFF) 44-dim.…”

“…feature vector which is combined into one feature set with the five kinds: 'the distribution of the block median,' 'the occurrence of the block-center gray level,' 'the number of gray levels in a block,' 'the distribution of the block-center gray level in the sorted gray levels,' and 'the first occurrence of the block-center gray level in the sorted gray levels' for MF images. Rhee [9] proposed an ensemble 15-dim. feature vector with the three characteristics of the feature set: 'the Canny edge' [10], 'the Prewitt gradient' [11], and the 'Hu invariant moments' [12] for MFD too.…”