Identifying color image origin using curvelet transform

Current prominent camera identification methods use wavelet-based filter to extract photo-response non-uniformity (PRNU) noise as camera fingerprint. However, these noise features in heavily textured images can not be extracted by using wavelet-based filter effectively. In this paper, we propose a new camera identification method that uses curvelet-based filter to extract noise features in heavily textured images or non-heavily textured image. Because curvelet transform allows an optimal sparse representation of objects with C² singularities, curvelet-based filter can extract the noise features in heavily textured images more effectively than wavelet-based filter. To increase the recognition rate for heavily textured images, we differentiate heavily textured images from non-heavily textured images by using the bivariate kurtosis of an image, and Neyman-Pearson decision is used to determine different decision thresholds.