Abstract
In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.
Highlights
In an era of globalization, social networks such as Facebook, Twitter, and Instagram are widely used in our daily lives and a huge number of photos are uploaded to these networks everyday.Further, it becomes easy even for unpracticed users to manipulate digital images without leaving any perceptible trace
The experimental results show that the proposed method outperforms state-of-the-art techniques in image forgery classification and localization accuracy
To evaluate the performance of the proposed image forgery clustering algorithm, we carry out the experiments to estimate mean average precision (MAP) of image retrieval in 3 different scenarios related to cluster formation of Algorithm 1
Summary
In an era of globalization, social networks such as Facebook, Twitter, and Instagram are widely used in our daily lives and a huge number of photos are uploaded to these networks everyday. Many researchers have put considerable effort into detecting and localizing tampered regions of image forgery. In most cases, forgery detection and localization algorithms were designed considering only one of two forgery types, copy-move and image splicing. We propose an image forgery detection and localization algorithm that can handle both types of image forgeries simultaneously. The image forgery clustering algorithm classifies input images into distinct clusters, each of which consists of one authentic image and all the spliced and. The cluster centroid is used to classify the image forgeries and localize the tampered regions. The experimental results show that the proposed method outperforms state-of-the-art techniques in image forgery classification and localization accuracy.
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