Two-stream encoder-decoder network for localizing image forgeries

This paper proposes a novel two-stream encoder-decoder network that utilizes both the high-level and the low-level image features for precisely localizing forged regions in a manipulated image. This is motivated by the fact that the forgery creation process generally introduces both the high-level artefacts (e.g., unnatural contrast) and the low-level artefacts (e.g., noise inconsistency) to the forged images. In the proposed two-stream network, one stream learns the low-level manipulation-related features in the encoder side by extracting noise residuals through a set of high-pass filters in the first layer. In the second stream, the encoder learns the highlevel image manipulation features from the input image RGB values. The coarse feature maps each encoder are upsampled by the corresponding decoder network to produce the dense feature maps. The dense feature maps of the two streams are concatenated and fed to a final convolutional layer with sigmoidal activation to produce the pixel-wise prediction. We have carried out experimental analyses on multiple standard forensics datasets to evaluate the performance of the proposed method. The experimental results show the efficacy of the proposed method with respect to the state-of-the-art.

Automated summary

This paper presents a two-stream encoder-decoder network that utilizes both high-level and low-level image features to precisely localize forged regions in manipulated images. By learning different levels of features in two streams, it improves the accuracy of detecting forged regions.