An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction |
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| Affiliation: | 1. Research Scholar, Department of ECE, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India;2. Professor, Department of ECE, R.V.R & J.C College of Engineering, Guntur, Andhra Pradesh, India;1. School of Data and Computer Science,Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China;2. College of Information Science and Technology, Jinan University, Guangzhou 510632, China;1. Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan;2. Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan;3. School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China;4. College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;1. Department of Electronics & Communication Engineering Om Sterling Global University, Hisar;2. Department of Electronics & Communication Engineering Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India;3. Department of Virtualization, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India;4. Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia;5. Department of Electrical and Electronics Engineering, BoluAbantIzzetBaysal University, Bolu, Turkey;1. Department of Electrical Engineering, Chinese Naval Academy, Taiwan;2. Department of Computer Science and Information Engineering, Chien Hsin University of Science and Technology, Jhongli 320, Taiwan |
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| Abstract: | Copy-move forgery detection (CMFD) is the process of determining the presence of copied areas in an image. CMFD approaches are mainly classified into two groups: keypoint-based and block-based techniques. In this paper, a new CMFD approach is proposed on the basis of both block and keypoint based approaches. Initially, the forged image is partitioned into non overlapped segments utilizing adaptive watershed segmentation, wherein adaptive H-minima transform is used for extracting the markers. Also, an Adaptive Galactic Swarm Optimization (AGSO) algorithm is used to select optimal gap parameter while selecting the markers for reducing the undesired regional minima, which can increase the segmentation performance. After that, the features from every segment are extracted as segment features (SF) using Hybrid Wavelet Hadamard Transform (HWHT). Then, feature matching is performed using adaptive thresholding. The false matches or outliers can be removed with the help of Random Sample Consensus (RANSAC) algorithm. Finally, the Forgery Region Extraction Algorithm (FREA) is utilized for detecting the copied portion from the host image. Experimental results indicate that the proposed scheme find out image forgery region with Precision = 92.45%; Recall = 93.67% and F1 = 92.75% on MICC-F600 dataset and Precision = 94.52%; Recall = 95.32% and F1 = 93.56% on Bench mark dataset at pixel level. Also, it outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation. |
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| Keywords: | Copy-move forgery detection Segments Adaptive Galactic Swarm Optimization RANSAC Adaptive thresholding |
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