ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Pattern Recognition
Volume 8 - 2025 | doi: 10.3389/frai.2025.1655073
Multi-scale and Depth-Supervised Network for Image Splicing Localization
Provisionally accepted
- Guangxi Normal University, Guilin, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
When maliciously tampered images are disseminated in the media, they can potentially cause adverse effects, and even jeopardize national security. Therefore, it is necessary to investigate effective methods to detect the tampered images. As a challenging task, the localization of image splicing tampering investigates whether an image contains tampered regions spliced from another image. Given the neglect of global information interactions of existing methods, a multi-scale and deeply supervised image splicing tampering localization network is proposed. The proposed network is based on an encoder-decoder architecture, where the decoder uses different levels of feature map to supervise the locations of splicing, enabling pixel-wise prediction of the tampered regions. Moreover, a multi-scale feature extraction module is utilized between the encoder and decoder, which expands the global view of the network, hence enables more effective differentiation between tampered and non-tampered regions. F1 scores of 0.891 and 0.864 were achieved using the CASIA and COLUMB datasets, respectively, and the proposed model is able to accurately locate the tampered regions.
Keywords: Image forensics, Image splicing, multi-scale, Encoder-decoder, deep learning
Received: 27 Jun 2025; Accepted: 04 Sep 2025.
Copyright: © 2025 Qin, Liang, Luo, Liu, Fu and Ouyang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yuling Luo, Guangxi Normal University, Guilin, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.