Video authentication using relative correlation information and SVM

Abstract

Video authentication is often presented as evidence in many criminal cases. Therefore, the authenticity of the video data is of paramount interest. This paper presents an intelligent video authentication algorithm using support vector machine. The proposed algorithm does not require the computation and storage of secret key or embedding of watermark. It computes the local relative correlation information and classifies the video as tampered or non-tampered. Performance of the proposed algorithm is not affected by acceptable video processing operations such as compression and scaling and effectively classifies the tampered videos. On a database of 795 videos, the proposed algorithm outperforms the existing algorithm by 18.5%. © 2008 Springer-Verlag Berlin Heidelberg.