By recruiting sensor-equipped smartphone users to report sensing data, mobile crowdsensing (MCS) provides location-based services such as environmental monitoring. However, due to the distributed and potentially selfish nature of smartphone users, mobile crowdsensing applications are vulnerable to faked sensing attacks by users who bid a low price in an MCS auction and provide faked sensing reports to save sensing costs and avoid privacy leakage. In this paper, the interactions among an MCS server and smartphone users are formulated as a mobile crowdsensing game, in which each smartphone user chooses its sensing strategy such as its sensing time and energy to maximize its expected utility while the MCS server classifies the received sensing reports and determines the payment strategy accordingly to stimulate users to provide accurate sensing reports. Nash equilibrium (NE) of a static MCS game is evaluated and a closed-form expression for the NE in a special case is presented. Moreover, a dynamic mobile crowdsensing game is investigated, in which the sensing parameters of a smartphone are unknown by the server and the other users. A Q-learning discriminated pricing strategy is developed for the server to determine the payment to each user. Simulation results show that the proposed pricing mechanism stimulates users to provide high-quality sensing services and suppress faked sensing attacks.