Community detection, which focuses on cluster vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like data missing and adversarial attack. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, a concept of robust enhancement is proposed for community detection, with two algorithms presented: one is named robust enhancement via genetic algorithm (RobustECD-GA), in which the modularity and the number of clusters are used to design a fitness function to solve the resolution limit problem; the other is called robust enhancement via similarity ensemble (RobustECD-SE), integrating multiple information of community structures captured by various vertex similarities, which scales well on large-scale networks. Comprehensive experiments on real-world networks demonstrate, by comparing with two traditional enhancement strategies, that the new methods help six representative community detection algorithms achieve more significant performance improvement. Moreover, experiments on the corresponding adversarial networks indicate that the new methods could also optimize the network structure to a certain extent, achieving stronger robustness against adversarial attack.