ResTran: Long Distance Relationship on Image Forgery Detection

dc.contributor.authorRao J.
dc.contributor.authorTeerakanok S.
dc.contributor.authorUehara T.
dc.contributor.otherMahidol University
dc.date.accessioned2023-11-21T18:01:24Z
dc.date.available2023-11-21T18:01:24Z
dc.date.issued2023-01-01
dc.description.abstractImage tampering detection is becoming more and more important in image forensics, especially in today's society where advanced image editing tools are becoming more and more popular. At the same time, with the rapid growth of digital image technology and multimedia data, it is very important to ensure the authenticity and integrity of visual information. Therefore, to address this issue, we propose a novel image tampering detection method using a deep convolutional network (ResNet) combined with a Transformer decoding layer. While traditional algorithms only solve a single tamper detection problem, our method can detect multiple tampering means. We use ResNet as the backbone of feature extraction to obtain rich and hierarchical features from input images. We then employ Transformer's decoding layer to process these features, enabling the model to capture long-distance dependencies and complex patterns, which we believe can further improve the accuracy of image tampering detection. In the experiments, we conduct experiments on three datasets (CASIA, NIST, and IMD2020) to verify the performance of the model. According to the experimental results, our model performed well on the CASIA and IMD2020 datasets, and also achieved good results in the NIST dataset. Furthermore, we also test two main types of image tampering: Copy-move and Splicing. Our model performs very well in detecting the Splicing type of image tampering. The experimental results show that this study fully demonstrates the potential and effectiveness of deep convolutional networks (such as ResNet) and Transformer decoding layers in image tampering detection, and also verifies the high performance and excellent generalization ability of the model.
dc.identifier.citationIEEE Access Vol.11 (2023) , 120492-120501
dc.identifier.doi10.1109/ACCESS.2023.3327761
dc.identifier.eissn21693536
dc.identifier.scopus2-s2.0-85176394840
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/91102
dc.rights.holderSCOPUS
dc.subjectComputer Science
dc.titleResTran: Long Distance Relationship on Image Forgery Detection
dc.typeArticle
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176394840&origin=inward
oaire.citation.endPage120501
oaire.citation.startPage120492
oaire.citation.titleIEEE Access
oaire.citation.volume11
oairecerif.author.affiliationRitsumeikan University Biwako-Kusatsu Campus
oairecerif.author.affiliationMahidol University

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