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author:

Chen, Y. (Chen, Y..) [1] | Cheng, H. (Cheng, H..) [2] | Wang, H. (Wang, H..) [3] | Liu, X. (Liu, X..) [4] | Chen, F. (Chen, F..) [5] | Li, F. (Li, F..) [6] | Zhang, X. (Zhang, X..) [7] | Wang, M. (Wang, M..) [8]

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Scopus

Abstract:

Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.  © 1991-2012 IEEE.

Keyword:

Attention mechanism Convolutional neural network Feature fusion Image manipulation localization

Community:

  • [ 1 ] [Chen Y.]Fuzhou University, Colleage of Computer and Data Science, Fujian, Fuzhou, 350108, China
  • [ 2 ] [Cheng H.]Fuzhou University, School of Mathematics and Statistics, Fujian, Fuzhou, 350108, China
  • [ 3 ] [Wang H.]Fuzhou University, School of Mathematics and Statistics, Fujian, Fuzhou, 350108, China
  • [ 4 ] [Liu X.]Fuzhou University, Colleage of Computer and Data Science, Fujian, Fuzhou, 350108, China
  • [ 5 ] [Chen F.]Fuzhou University, Colleage of Computer and Data Science, Fujian, Fuzhou, 350108, China
  • [ 6 ] [Li F.]Shanghai University of Electric Power, College of Computer Science and Technology, Shanghai, 201306, China
  • [ 7 ] [Zhang X.]Fudan University, School of Computer Science, Shanghai, 200433, China
  • [ 8 ] [Wang M.]Fuzhou University, School of Mathematics and Statistics, Fujian, Fuzhou, 350108, China

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Source :

IEEE Transactions on Circuits and Systems for Video Technology

ISSN: 1051-8215

Year: 2024

Issue: 2

Volume: 35

Page: 1591-1601

8 . 3 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 4

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