In existing image manipulation localization methods, the receptive field of standard convolution is limited, and during feature transfer, it is easy to lose high-frequency information about traces of manipulation. In addition, during feature fusion, the use of fixed sampling kernels makes it difficult to focus on local changes in features, leading to limited localization accuracy. This paper proposes an image manipulation localization method based on dual-branch hybrid convolution. First, a dual-branch hybrid convolution module is designed to expand the receptive field of the model to enhance the feature extraction ability of contextual semantic information, while also enabling the model to focus more on the high-frequency detail features of manipulation traces while localizing the manipulated area. Second, a multi-scale content-aware feature fusion module is used to dynamically generate adaptive sampling kernels for each position in the feature map, enabling the model to focus more on the details of local features while locating the manipulated area. Experimental results on multiple datasets show that this method not only effectively improves the accuracy of image manipulation localization but also enhances the robustness of the model.