Cargando…

SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMF...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Kaiqi, Yuan, Xiaochen, Xie, Zhiyao, Xiang, Yan, Huang, Guoheng, Feng, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385401/
https://www.ncbi.nlm.nih.gov/pubmed/37514723
http://dx.doi.org/10.3390/s23146430
_version_ 1785081397443035136
author Zhao, Kaiqi
Yuan, Xiaochen
Xie, Zhiyao
Xiang, Yan
Huang, Guoheng
Feng, Li
author_facet Zhao, Kaiqi
Yuan, Xiaochen
Xie, Zhiyao
Xiang, Yan
Huang, Guoheng
Feng, Li
author_sort Zhao, Kaiqi
collection PubMed
description With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.
format Online
Article
Text
id pubmed-10385401
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-103854012023-07-30 SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection Zhao, Kaiqi Yuan, Xiaochen Xie, Zhiyao Xiang, Yan Huang, Guoheng Feng, Li Sensors (Basel) Article With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods. MDPI 2023-07-15 /pmc/articles/PMC10385401/ /pubmed/37514723 http://dx.doi.org/10.3390/s23146430 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Kaiqi
Yuan, Xiaochen
Xie, Zhiyao
Xiang, Yan
Huang, Guoheng
Feng, Li
SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
title SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
title_full SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
title_fullStr SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
title_full_unstemmed SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
title_short SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
title_sort spa-net: a deep learning approach enhanced using a span-partial structure and attention mechanism for image copy-move forgery detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385401/
https://www.ncbi.nlm.nih.gov/pubmed/37514723
http://dx.doi.org/10.3390/s23146430
work_keys_str_mv AT zhaokaiqi spanetadeeplearningapproachenhancedusingaspanpartialstructureandattentionmechanismforimagecopymoveforgerydetection
AT yuanxiaochen spanetadeeplearningapproachenhancedusingaspanpartialstructureandattentionmechanismforimagecopymoveforgerydetection
AT xiezhiyao spanetadeeplearningapproachenhancedusingaspanpartialstructureandattentionmechanismforimagecopymoveforgerydetection
AT xiangyan spanetadeeplearningapproachenhancedusingaspanpartialstructureandattentionmechanismforimagecopymoveforgerydetection
AT huangguoheng spanetadeeplearningapproachenhancedusingaspanpartialstructureandattentionmechanismforimagecopymoveforgerydetection
AT fengli spanetadeeplearningapproachenhancedusingaspanpartialstructureandattentionmechanismforimagecopymoveforgerydetection