Deep learning-based image inpainting methods leave little trace information in the tampered image, which makes forensics extremely difficult. There are few studies on inpainting forensics, and the localization of tampered areas is inaccurate. Therefore, a dynamic feature fusion forensics network (DF³Net) was proposed for locating tampered areas that have undergone deep image inpainting operations. Firstly, the network expanded single input to multi-inputs by exploiting different tamper trace enhancement methods including spatial rich model (SRM) filtering, high-pass filtering of spatial domain, and high-pass filtering of frequency domain. Then, a dynamic feature fusion module was proposed to extract effective inpainting trace features and conduct dynamic feature fusion. Secondly, the network adopted the encoder-decoder architecture as basic framework, and a multi-scale feature extraction module was added at the end of the encoder to obtain contextual information at different scales. Finally, a spatially weighted channel attention module was designed for the skip connection between encoder and decoder, so as to achieve a focused supplementation of the lost details. Experimental results show that DF³Net could locate the tampered areas more accurately than existing methods on different datasets, and was robust against JPEG compression and Gaussian noise.