Forgery image detection of Gaussian filtering by support vector machine using edge characteristics
Kang Hyeon Rhee · 2017
For a design of the Gaussian filtering (GF) detection (GFD) in the tampered digital images, this paper presents three kinds of the new feature vector which are extracted from the edge ratios and the parameters of Hough peaks. In the proposed algorithm, the formed 10-dim. feature vector is trained in SVM (Support Vector Machine) for the GFD. In the experiment, the performance of the proposed GFD scheme is measured in GFw (window = {3 × 3, 5 × 5, compound (3 × 3, 5 × 5)}, σ= 0.5) images versus the median filtering (MF3: window = 3 × 3), the original (ORI), the average filtering (AVE3: window = 3 × 3), and the JPG90 (Quality Factor = 90) images, respectively. However, the measured performances of the AUC by the sensitivity (TP: True Positive rate) and 1-specificity (FP: False Positive rate) is above 0.9. Thus, it is confirmed that the grade evaluation of the proposed algorithm is rated as “Excellent (A)”.