A New Technique for Video Surveillance

Video authentication has gained much attention in recent years. However many existed authentication techniques have their own advantages and obvious drawbacks; we propose a novel authentication technique which uses an intelligent approach for video authentication. Our methodology is a learning based methodology which uses SVM (support vector machine) for learning and classification purpose and a video database as sample data. The proposed algorithm does not require the computation and storage of any digital signature or embedding of any watermark. Therefore it works for raw videos (videos captured in any situation), and useful for real life application of authentication. It covers all kinds of tampering attacks of spatial and temporal tampering. It uses a database of more than 1200 tampered and nontampered videos and gives excellent results with 93.5% classification accuracy.