A Visually Attentive Splice Localization Network with Multi-Domain
  Feature Extractor and Multi-Receptive Field Upsampler
Ankit Yadav Dinesh Kumar Vishwakarma
Image splice manipulation presents a severe challenge in today's society.
With easy access to image manipulation tools, it is easier than ever to modify
images that can mislead individuals, organizations or society. In this work, a
novel, "Visually Attentive Splice Localization Network with Multi-Domain
Feature Extractor and Multi-Receptive Field Upsampler" has been proposed. It
contains a unique "visually attentive multi-domain feature extractor" (VA-MDFE)
that extracts attentional features from the RGB, edge and depth domains. Next,
a "visually attentive downsampler" (VA-DS) is responsible for fusing and
downsampling the multi-domain features. Finally, a novel "visually attentive
multi-receptive field upsampler" (VA-MRFU) module employs multiple receptive
field-based convolutions to upsample attentional features by focussing on
different information scales. Experimental results conducted on the public
benchmark dataset CASIA v2.0 prove the potency of the proposed model. It
comfortably beats the existing state-of-the-arts by achieving an IoU score of
0.851, pixel F1 score of 0.9195 and pixel AUC score of 0.8989.