Compact and robust video fingerprinting using sparse represented features

In this paper, we propose a compact and robust video fingerprinting scheme by using sparse represented features (SRF). The SRF are extracted by a two dimensional matching pursuit decomposition (2D-MPD) method. The motivation of using sparse features is that the sparse coding method can significantly reduce the data dimensionality and effectively retain the structure of the images. To further reduce the length of the fingerprint, a two-stage cascade SVD method is applied. The SVD-based feature extraction method can improve the robustness to certain attacks, such as geometric attack. Then, a locally adaptive quantization (AQ) method which considers the local probability distribution of the sample is applied. This method can quantize the real-valued fingerprints into binary bits without degrading the detection performance too much. At last, a secret key based interleaving method is applied to the binary fingerprints. The interleaving can enlarge the Hamming distance of the fingerprints, so the detection performance is improved. According to the experimental results, the proposed method offers a favourable robustness versus discriminability tradeoff over the state-of-the-art video fingerprint methods.