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: {3×3, 5×5}), JPEG compressed (quality factor: {90, 70}) and average filtered (window size: 3×3). 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)”.
Highlights
In the current media society, a large amount of multimedia content is distributed
For the spliced image forensics, a new image forensics detection (IFD) scheme is proposed, in which the feature vector extracted from the texture analysis of median filter residual (MFR)
The experimental results of the proposed IFD scheme are presented with the four test items: ‘Classification accuracy,’ ‘Area under the ROC curve (AUC),’ ‘PTP at PFP = 0.01’, and ‘Pe.’ the experimental results are compared to those of [7], [8] to verify the performance, where PTP (True Positive rate: Sensitivity), PFP (False Positive rate: 1-Specificity), PFN (False Negative rate)
Summary
In the current media society, a large amount of multimedia content is distributed. copyright protection of contents has emerged as an essential issue. To summarize with the words, the median filtering is much manipulation in image forensics, and feature definitions are needed to detect a spliced region. A proposed IFD scheme to detect the spliced image forensics by using texture analysis of the image itself for outgrowing conventional methods: the MFR AR and the MFR 2D-AR, which depend on statistical processing. Be comprehended the main contribution of this paper is as follows: 1) For detecting an unknown state of the spliced image, its MFR is decomposed to obtain the local entropy and the local range, and their texture is analyzed. For the spliced image forensics, a new IFD scheme is proposed, in which the feature vector extracted from the texture analysis of MFR.
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