Unscrupulous users increasingly find Online Social Networking (OSN) platforms as lucrative targets for malicious activities, such as sending spam and spreading malware. The profitability of such activities and the fact that a large portion of the OSN communication takes place over symmetric social links. Unwanted friend requests in online social networks (OSNs), also known as friend spam, are among the most evasive malicious activities. Friend spam can result in OSN links that do not correspond to social relationship among users, thus pollute the underlying social graph upon which core OSN functionalities are built, including social search engine, ad targeting, and OSN defense systems. To effectively detect the fake accounts that act as friend spammers, we propose a system called vote trust. It systems from the observation on social rejections in OSNs, i.e., even we will maintained fake accounts inevitably have their friend requests rejected or they are reported by legitimate users. Our key insight is to partition the social graph into two regions such that the aggregate acceptance rate of friend requests from one region to the other is minimized. This design leads to reliable detection of a region that comprises friend spammers, regardless of the request collusion among the spammers. Meanwhile, it is resilient to other strategic manipulations. Then extend the project to protect the image piracy using water marking techniques in online social networks.