Combined Zernike Moment and Multiscale Analysis for Tamper Detection in Digital Images
The paper proposes a new approach as a combination of the multiscale analysis and the Zernike moment based for detecting tampered image with the formation of copy – move forgeries. Although the traditional Zernike moment based technique has proved its ability to detect image forgeries in block based method, it causes large computational cost. In order to overcome the weakness of the Zernike moment, a combination of multiscale and Zernike moments is applied. In this paper, the wavelets and curvelets are candidates for multiscale role in the proposed method. The Wavelet transform has successful in balancing the running time and precision while the precision of the algorithm applied the Curvelets does not meet the expectation. The comparison and evaluation of the performance between the Curvelets analysis and the Wavelets analysis combining with the Zernike moments in a block based forgery detection technique not only prove the effective of the combination of feature extraction and multiscale but also confirm Wavelets to be the best multiscale candidate in copy-move detection.
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