Rotation Invariant Localization of Duplicated Image Regions Based on Zernike Moments

This paper proposes a forensic technique to localize duplicated image regions based on Zernike moments of small image blocks. We exploit rotation invariance properties to reliably unveil duplicated regions after arbitrary rotations. We devise a novel block matching procedure based on locality sensitive hashing and reduce false positives by examining the moments´ phase. A massive experimental test setup benchmarks our algorithm against state-of-the-art methods under various perspectives, examining both pixel-level localization and image-level detection performance. By taking signal characteristics into account and distinguishing between “textured” and “smooth” duplicated regions, we find that the proposed method outperforms prior art in particular when duplicated regions are smooth. Experiments indicate high robustness against JPEG compression, blurring, additive white Gaussian noise, and moderate scaling.