Robust video fingerprints based on subspace embedding

Abstract

We present a novel video fingerprinting method based on subspace embedding. The proposed method is particularly robust against frame-rate conversion attacks and geometric attacks among other attacks including compression and spatial scaling. Using a sliding window, we extract fingerprints from a group of subsequent video frames. For the generation of the fingerprints, we first calculate the basis vectors of a coarse representation of this group of frames using a singular value decomposition (SVD). Then, we project the coarse representation of the video frames onto a subset of the basis vectors. Thus, we obtain a subspace representation of the input video frames. Finally, we extract the fingerprint bits by projecting a temporal average of these representations onto pseudorandom basis vectors. Since the subspace is estimated from the input video data itself, any global attack on video such as rotation would result in a corresponding change in estimated basis vectors thereby preserving the subspace representation. We present experimental results on 250 hrs of video to show the robustness and sensitivity of the proposed signature extraction method.