  Online reviews are potent sources for industry owners and buyers, however
opportunistic people may try to destruct or promote their desired product by
publishing fake comments named spam opinion. So far, many models have been
developed to detect spam opinions, but none have addressed the issue of spam
attack. It is a way a smart spammer can deceive the system in a manner in which
he can continue generating spams without the fear of being detected and blocked
by the system. In this paper, the spam attacks are discussed. Moreover, a
robust graph-based spam detection method is proposed. The method respectively
estimates honesty, trust and reliability values of reviews, reviewers, and
products considering possible deception scenarios. The paper also presents the
efficiency of the proposed method as compared to other graph-based methods
through some case studies.
