Content Based Copy Detection is an alternative approach to invisible watermarking for tracking duplicate data. Primary stages are creating a database using the features belonging to the original data and searching query data in terms of its features in this database. Features must be robust against targeted attacks and discriminative enough to distinguish different content. In this work, we propose reducing the precision of feature values to attain robustness and increasing the number and dimension of features to attain discriminativity. To this end, we create a feature database using different features, which correspond to different information sources, together. We detect the original sources of the query videos in this database. which is composed of coarse features, by feature comparison. Effectiveness of the proposed method against various attacks is observed through experiments.