Efficient hybrid passive method for the detection and localization of copy-moveand spliced images


Abstract: Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images.Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have beenproposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously.However, a more e?icient method is still in demand to meet the day-to-day challenges to detect these forgeries at thesame time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binarypattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractionalparameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively.Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and splicedimages. Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas inthe image. Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE,and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively. To validatethe effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms ofROC, precision, recall,F1score,F2score, and accuracy. Moreover, the robustness of the proposed work is tested underrotation attack and better results are attained in comparison to the existing techniques.

Keywords: Local binary pattern, discrete fractional cosine transform, copy-move, splicing

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