the need for establishing authenticity of digital images is becoming inevitably important given the ease with which images may be tempered. Moreover, the images are tampered with such expertise that it is impossible, ...
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ISBN:
(纸本)9781538630044
the need for establishing authenticity of digital images is becoming inevitably important given the ease with which images may be tempered. Moreover, the images are tampered with such expertise that it is impossible, at least visually, to figure out if they are tampered or not. In recent years, copy-move forgery has emerged as one of the most researched topics in the field of image forensics. The common detection techniques used are either block based (for smoothed regions) or keypoint based (for non-smoothed regions), each having its own share of pros and cons. While a block based approach provides higher accuracy, it is computationally taxing and fails to handle geometric transformations. Similarly, a Keypoint based approach fails to deal with smoothed regions. Our hybrid approach handles these limitations in an intelligent and adaptive way. The image fed to our software tool is adaptively segmented into semantically meaningful non-overlapped regions/segments using Simple Linear Iterative Clustering (SLIC) algorithm. Feature points as keypoints are extracted using the Scale Invariant Feature Transform (SIFT) algorithm. Given these keypoints, a segment is classified as either smoothed or non-smoothed depending upon a predetermined threshold. Once done with this classification, the proposed hybrid approach engages one of the aforementioned detection strategies to detect image forgery. Accordingly, a block based approach is implemented using Zernike moments with the matchingalgorithm based on Euclidean distances, while a keypoint based approach is implemented using SIFT features in tandem with the flann matching algorithm for the detection of matching pairs. The proposed hybrid approach has been compared with the individual approaches for an optimal value of threshold. Experimental results on different types of original as well as forged images establish that our proposed approach is able to detect image forgery in smoothed and non-smoothed images with reasonable ac
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