A Fast and Efficient Method for Image Splicing Localization Using BM3D Noise Estimation
Image manipulation is the process of altering the content of an image with a particular intention or interest. By the developments in the camera industry and powerful editing tools, the image manipulation has become an easy task for even a less skilled person. Therefore, ensuring the authenticity of an image from an external source is necessary before using it. The proposed method deals with detecting an image forgery method known as splicing. It is a comparatively fast and reliable method for image splicing localization. The variation in noise level of different segments, non-overlapped segmentation using simple linear iterative clustering (SLIC), is estimated with the help of Block-Matching and 3D Collaborative Filtering (BM3D). The estimated noise variation is used to cluster the segments where the clusters with relatively higher noise level are located as suspicious or forged regions. It is found that the proposed method takes comparatively less time for execution and the false positive rate is also considerably low. Experimental results show better performance of the proposed method against existing methods of image splicing forgery detection.
KeywordsImage splicing SLIC BM3D Noise level K-means clustering
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