Sensor Fingerprint Identification Through Composite Fingerprints and Group Testing

Abstract

The photo response non-uniformity noise associated with an imaging sensor has been shown to be a unique and persistent identifier that can be treated as the sensor's digital fingerprint. The method for attributing an image to a particular camera, however, is not suitable for source identification due to efficiency considerations, which is a one-to-many matching of a single fingerprint against a database of fingerprints. To address this problem, we propose a group-testing approach based on the notion of composite fingerprints (CFs), generated by combining many actual fingerprints together into a single fingerprint. Our technique organizes a database of fingerprints into an unordered binary search tree, wherein each internal node is represented by a fingerprint composited from all the fingerprints at the leaf nodes in the subtree beneath that node. Different search strategies are considered, and the performance is analyzed analytically and verified using numerical simulations as well as experimental results. Our results are presented in comparison with the linear search-based approach that utilizes fingerprint digests for more effective computation. Results obtained under the best achievable accuracy showed that the proposed method yields a lower overall computational cost. It is also shown that by complementary use of the fingerprint dimension reduction and CF-based search tree approaches, it is possible to further improve the search efficiency.