research-article

Image splicing tamper detection based on two-channel dilated convolution

Authors:

Chengxiang WangAuthors Info & Claims

ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering Conference

Pages 37 - 43

https://doi.org/10.1145/3523181.3523187

Published: 18 April 2022 Publication History

Abstract

Image splicing tampering detection can provide technical support for the identification of authenticity and integrity of images in various fields by using specific methods to quickly detect whether tampering has occurred in images. Most of the existing splicing tamper detection methods pay more attention to the detection accuracy of tamper image, ignore the importance of tamper region location, and can only provide rough tamper region location map. To solve the above problems, we proposed an image splicing tamper detection model based on dual-channel dilated convolution, which combined the deep features and shallow features of the image to accurately locate the tamper area. In this model, the first channel extracts noise features through a set of high-pass filters to find noise inconsistencies between the real area and the tampered area. The second channel extracts RBG image features through dilated convolution and locates the tampered area in combination with the attention mechanism. Then, for each region of interest, the features generated by the two channels are fused together through bilinear pooling layer. Finally, tamper classification and boundary box regression are performed. We performed experimental analysis on NIST16 and CASIA datasets, and compared with the other five models, this model has better positioning performance, with the F1 index increased by 0.026 and 0.051, respectively, compared with other advanced methods.

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Cited By

Xu ZZhang CQi JLi XYao BWang L(2024)A dual-difference change detection network for detecting building changes on high-resolution remote sensing imagesGeocarto International10.1080/10106049.2024.232208039:1Online publication date: 13-Mar-2024
https://doi.org/10.1080/10106049.2024.2322080
Gan ZJiang WChai XSong YYu J(2023)End-to-end image splicing localization based on multi-scale features and residual refinement moduleJournal of Electronic Imaging10.1117/1.JEI.32.6.06301032:06Online publication date: 1-Nov-2023
https://doi.org/10.1117/1.JEI.32.6.063010

Image splicing tamper detection based on two-channel dilated convolution
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Other conferences

ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering Conference

February 2022

202 pages

ISBN:9781450387453

DOI:10.1145/3523181

Copyright © 2022 ACM.

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Publication History

Published: 18 April 2022

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Gansu University of Political Science and Law
Science and Technology Department of Gansu Province

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ASSE' 22

ASSE' 22: 2022 3rd Asia Service Sciences and Software Engineering Conference

February 24 - 26, 2022

Macau, Macao

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Cited By

Xu ZZhang CQi JLi XYao BWang L(2024)A dual-difference change detection network for detecting building changes on high-resolution remote sensing imagesGeocarto International10.1080/10106049.2024.232208039:1Online publication date: 13-Mar-2024
https://doi.org/10.1080/10106049.2024.2322080
Gan ZJiang WChai XSong YYu J(2023)End-to-end image splicing localization based on multi-scale features and residual refinement moduleJournal of Electronic Imaging10.1117/1.JEI.32.6.06301032:06Online publication date: 1-Nov-2023
https://doi.org/10.1117/1.JEI.32.6.063010

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