A review of video fingerprints invariant to geometric attacks

Video fingerprints can help us identify a large amount of video on the Internet and enable interesting services to the end user. One of the main challenges for video fingerprints is for them to be robust against intentional/unintentional geometric modifications on the content such as scaling, aspect ratio conversion, rotation and cropping. In this paper, we review a number of fingerprinting methods proposed in literature that are particularly designed to be robust against such modifications. We also present two approaches that we adopted. One that is based on estimation of Singular Value Decomposition (SVD) bases from a window of past video frames (Approach 1) and another that is based on extraction of moment invariant features from concentric circular regions and doesn’t require any specific transform (Approach 2). While both approaches provide the desired robustness against geometric modifications, Approach 1 is computationally more intensive than Approach 2 as the SVD bases are updated for every input frame at 12fps. It also requires a longer query clip than Approach 2 for reliable identification. We present results comparing the performance of both of these approaches for a 150hr video database.