This paper proposes a real-time deepfake framework to assist users use deep forgery to conduct live streaming, further to protect privacy and increase interesting by selecting different reference faces to create a non-existent fake face. Nowadays, because of the demand for live broadcast functions such as selling goods, playing games, and auctions, the opportunities for anchor exposure are increasing, which leads live streamers pay more attention to their privacy protection. Meanwhile, the traditional technology of deepfake is more likely to infring on the portrait rights of others, so our framework supports users to select different face features for facial tampering to avoid infringement. In our framework, through feature extractor, heatmap transformer, heatmap regression and face blending, face reenactment could be confirmed effectively. Users can enrich the personal face feature database by uploading different photos, and then select the desired picture for tampering on this basis, and finally real-time tampering live broadcast is achieved. Moreover, our framework is a closed loop self-adaptation system as it allows users to update the database themselves to extend face feature data and improve conversion efficiency.