In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would cause severe social impact and become a real threat to not only celebrities but also ordinary people. One way to defend the DeepFake is to disrupt the DeepFake generation by adding human-imperceptible perturbations to source inputs. Adding perturbations to the source inputs will make DeepFake results distorted from the perspective of human eyes. However, the existing methods only ensure that fake data will be visually distorted rather than ensuring that the disrupted data (fake data) can also be detected by DeepFake detectors for automation process. If DeepFake detectors are still easily getting spoofed by the disrupted data, then the existing methods cannot be used because this will result in huge labour cost when examining a large amount of data manually. However, we argue that the detectors do not have a similar perspective as human eyes, and thus the detectors might still be spoofed by the disrupted data. Besides, the existing disruption methods rely on iteration-based perturbation generation algorithms, which is time-consuming. In this paper, we propose a novel DeepFake disruption algorithm called "DeepFake Disrupter". By training a perturbation generator, we can add the human-imperceptible perturbations to source images that need to be protected without any backpropagation update. The DeepFake results of these protected source inputs would not only look unrealistic by the human eye but also can be distinguished by DeepFake detectors easily. For example, experimental results show that by adding our trained perturbations, fake images generated by StarGAN can result in a 10~20% increase in F1-score evaluated by various DeepFake detectors.