Image manipulation localization is a binary segmentation task that sensitive to the tampered artifacts other than awareness of the object. Thus, both traditional and learning-based methods highly rely on hand-crafted features. However, these specifically-defined features limit the ability of the network for general scenes. To tackle this problem, we propose a dual homology-aware generative adversarial network (DH-GAN), a novel GAN-based framework to localize the manipulated region. Firstly, we localize the forgery region via re-calibrating the multi-scale encoded features with a selective pyramid generator. Then, we perform the homology identification in the discriminator. The proposed homology-aware discriminators contain a stack of masked convolution (MConv) layers and learn to identify the real/fake of the segmented pixels on the predicted/target masked image in a hard-gating manner. Overall, the networks are optimized under a standard GAN. Experiments show that the proposed method outperforms other state-of-the-art algorithms on four popular image manipulation datasets. •Avoiding the use of hand-crafted features for image manipulation localization.•Learning generalized features by identifying the homology of the pixels.•Re-calibrating multi-scale features with cross-scale information interaction.