Abstract: Copy-move is a common attacking method for producing image forgeries. A certain area of an image is copied and then pasted over another area of the image to conceal important information or construct a fake scene. In recent years, in order to prevent copy-move attack from being abused in illegal activities, the methods for copy-move forgery detection have been developed rapidly. These forensics methods play positive roles in maintaining social order and securing information. In this paper, based on conditional generative adversarial networks (cGANs), a novel method is proposed for the detection of copy-move forgery. To boost the detection performance, the loss function of cGANs is optimally designed, and an appropriate amount of weakly supervised samples are utilized to improve the network. Unlike most existing detection methods, the proposed method can not only detect similar regions in an image, but also effectively distinguish between source forgery regions and target forgery regions. Extensive experimental results show that the proposed method remarkably outperforms the compared methods in detection accuracy.