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.