Center region aware cross former for image forgery localization

Yahui Wu; Xuchao Gong; Miao Zhang; Peng Zhang

doi:10.1117/12.3023273

28 March 2024 Center region aware cross former for image forgery localization

Yahui Wu, Xuchao Gong, Miao Zhang, Peng Zhang

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130911Y (2024) https://doi.org/10.1117/12.3023273
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Image forgery has been a serious issue in real life during this boosting big data era. There have been many methods depending on detecting footprints such as edge inconsistency, camera noise, JPEG artifacts, etc., proposed. Unlike the existing methods, we propose a more general method, CRAC-formerNet to capture more stable manipulation traces, not only including RGB domain but also from high-frequency features generated from Steganalysis Rich Model (SRM) and District Cosine transformer in the frequency domain. A shared query is generated from features from both the RGB and frequency domains. The keys and values are generated from each domain respectively. Moreover, features of manipulated and manipulated regions should belong to different distributions, especially features in edge regions. We use a center contrastive loss to learn fine-grained features. A manipulated region proposal module is proposed for improving computing efficiency when calculating the loss. We empirically demonstrate that our method achieves competitive results on four benchmark image manipulation datasets.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yahui Wu, Xuchao Gong, Miao Zhang, and Peng Zhang "Center region aware cross former for image forgery localization", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130911Y (28 March 2024); https://doi.org/10.1117/12.3023273

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KEYWORDS

RGB color model

Transformers

Image segmentation

Education and training

Head

Semantics

Feature extraction

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