Fraudulent Image Recognition Using Stable Inherent Feature
To prove authenticity and originality of images, many techniques were recently released. This paper proposes a pixel based forgery detection technique for identifying forged images, which is an effective method for finding tampering in images. The proposed method detects splicing and copy-move forgery in images by locating the forged components in the input image. Splicing is the process of copying a component from an image and pasted to another image. Copy-move forgery is the process of copying a component from an image and pasted to another portion of the same image. To find the forged component in the input image, the noise variance remaining after denoising method and SURF features are used. In order to locate spliced component, image segmentation is done before finding the number of components in the image. For segmentation, segmentation based on combining spectral and texture features are used. To identify the number of components in the image, fuzzy c-means clustering is used. In order to locate copy-move forgery, SURF features are detected first and extracted for finding the similarity between keypoints. The experiment results show that the proposed method is very good at identifying whether an image is forged or not. The proposed method gives a high speed performance compared to the state-of-the-art methods. Results gained through the experiments on both manually edited images and visually realistic real images shows the effectiveness of the proposed method.
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