Wang, Z. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2023) WCBnet: Weighted convolutional block modelling of signed-value error levels for image-wise copy-move and splicing detection. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP) Proceedings. 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 27-29 Sep 2023, Poitiers, France. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9798350338942
Abstract
Image manipulation which can easily generate hard-to-perceive fake information by image editing tools has become a threat of spreading visual mis/disinformation. With the speed and growth of such visual information presence in social media with respect to the current geopolitical affairs, tools for highly accurate verification of the authenticity of images are vital for AI-based fact checking. This work presents an efficient convolutional neural network (CNN) based approach for image manipulation detection. Our method, called WCBnet, starts with extracting learned features from the signed-value error levels (SEL) of compressed images on hierarchical convolution blocks. This is followed by adaptively concatenating, weighting and fusing these multi-level features by considering self-attention over all blocks according to different error levels corresponding to different manipulation types. We evaluate the performance of the proposed approach with respect to common manipulation datasets and compare with the state-of-the-art. WCBnet trained using around 2500 images of CASIA 2.0 dataset, resulted in the best F1-score for CASIA 1.0, Defacto, Coverage and Columbia datasets after fine-tuning by a small portion of those datasets. On average WCBnet improves the F1 score with respect to the second-best performing methods by 27.5%, 34.3%, 16.2% and 6.1% for these four datasets, respectively.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP) Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | mis/disinformation; fact checking; manipulated image detection; self-attention; error-level analysis; feature reshaping |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jan 2024 16:56 |
Last Modified: | 03 Jan 2024 16:59 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/mmsp59012.2023.10337680 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206916 |