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

Abstract

In the image forensics, detection of Cut-Paste manipulation is complicated computing. In this paper, the texture analysis of the spliced image is used to detect image forensics. From the local entropy of the median filter residual (MFR) of the forged image, the feature set is extracted for the ground truth mask 'Find Gray level regional Maxima (FGM),' and 'Entropy-based Edge (EbE).' Also, from the local range, the feature set is extracted for ground truth mask {'Morphological-Open Image (MOI), and 'Morphological- Erosion Image (MOE)'}. The feature vector in this paper composed of the two MOIs, two MOEs, and one EbE. The defined novel feature vector trained on a cubic support vector machine (SVM) classifier for only the performance evaluation of the proposed scheme. The performance of the proposed image forensics detection (IFD) scheme was measured with the five transformed types of image: median filtered (window size: { 3\times 3 , 5\times 5 }), JPEG compressed (quality factor: {90, 70}) and average filtered (window size: 3\times 3 ). For the detection of the spliced image forensics, the region of Cut-Paste is classified by an input image only to the proposed scheme without the need for the trained SVM classifier. Throughout the experiment, the accuracy of median filtering detection was 98% over. Also, The area under the curve by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed IFD scheme approached to '1' with the trained cubic SVM classifier. Experimental results show high efficiency and performance to the spliced image. Therefore, the grade evaluation of the proposed scheme is 'Excellent ( A ).'