Calibrating Thresholds Based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection
Savita Walia1, Krishan Kumar2 

1Savita Walia, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.
2Krishan Kumar, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.

Manuscript received on 06 March 2019 | Revised Manuscript received on 13 March 2019 | Manuscript published on 30 July 2019 | PP: 3658-3663 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2083078219/19©BEIESP | DOI: 10.35940/ijrte.B2083.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: In this paper, prominent Keypoint based features are compared in order to analyze their reliability and efficiency against forgery detection. Four features specifically SURF, KAZE, Harris corner points and BRISK features are used individually on a set of images. The method includes four phases: Image pre-processing, Keypoint detection, feature vector description and feature vector matching. In feature matching, Max Ratio has been chosen as a varying parameter for calculating values of false positives and false negatives for each feature. Max Ratio defines the ratio for rejecting ambiguous matches of feature descriptors in the images. The optimal threshold value for Max Ratio is calibrated with the help of trade-off between detection accuracy and false positive ratio. The changes in false negative ratio and false positive ratio are picturized in order to find out optimal threshold for detection accuracy. ROC curves are also plotted for each feature at different values of Max Ratio and area under the ROC curves are calculated. The experiments are performed on two benchmark datasets, namely CASIA version 2.0 and MICC-F600. It has been perceived from experimental outcomes that KAZE features gave best values for all the performance metrics namely accuracy, precision, area under the ROC curve and F1-score with little compromise in time complexity, whereas Harris corner points gave the worst results as compared to rest of the features. Further, in order to improve the execution time, the computation of non-linear scale space process in KAZE can be simplified and GPU programming for real-time performance may also be used.
Index Terms: Digital Image Forensics, Image Forgery Detection, Passive Methods, Copy-Move, Key point Detection, Threshold Calibration.

Scope of the Article: Image Security