Multi-task SE-Network for Image Splicing Localization

Yulan ZHANG, Guopu ZHU, Ligang WU, Sam KWONG, Hongli ZHANG, Yicong ZHOU

Research output: Journal PublicationsJournal Article (refereed)peer-review

25 Citations (Scopus)

Abstract

Image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets.
Original languageEnglish
Pages (from-to)4828-4840
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume32
Issue number7
Early online date27 Oct 2021
DOIs
Publication statusPublished - Jul 2022
Externally publishedYes

Keywords

  • Image forensics
  • image splicing localization
  • low-level feature fusion
  • multi-task learning
  • squeeze and excitation attention module

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