Scale-Robust Compressive Camera Fingerprint Matching with Random Projections
Diego Valsesia, Giulio Coluccia, Tiziano Bianchi, Enrico Magli
40th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, April 19-24, 2015
Recently, we demonstrated that random projections can provide an extremely compact representation of a camera fingerprint without significantly affecting the matching performance. In this paper, we propose a new construction that makes random projections of camera fingerprints scale-robust. The proposed method maps the compressed fingerprint of a rescaled image to the compressed fingerprint of the original image, rescaled by the same factor. In this way, fingerprints obtained from rescaled images can be directly matched in the compressed domain, which is much more efficient than existing scale-robust approaches. Experimental results on the publicly available Dresden database show that the proposed technique is robust to a wide range of scale transformations. Moreover, robustness can be further improved by providing reference scales in the database, with a small additional storage cost.