An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

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.

Automated summary

A new Copy-Move Forgery Detection (CMFD) approach is proposed in this study, utilizing adaptive watershed segmentation and H-minima transform for feature extraction, and Hybrid Wavelet Hadamard Transform for segment feature extraction. The use of genetic algorithm and RANSAC algorithm helps in identifying image forgery region.