Effective Image Tampering Localization via Enhanced Transformer and
Co-attention Fusion
- URL: http://arxiv.org/abs/2309.09306v1
- Date: Sun, 17 Sep 2023 15:43:06 GMT
- Title: Effective Image Tampering Localization via Enhanced Transformer and
Co-attention Fusion
- Authors: Kun Guo, Haochen Zhu, Gang Cao
- Abstract summary: We propose an effective image tampering localization network (EITLNet) based on a two-branch enhanced transformer encoder.
The features extracted from RGB and noise streams are fused effectively by the coordinate attention-based fusion module.
- Score: 5.691973573807887
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Powerful manipulation techniques have made digital image forgeries be easily
created and widespread without leaving visual anomalies. The blind localization
of tampered regions becomes quite significant for image forensics. In this
paper, we propose an effective image tampering localization network (EITLNet)
based on a two-branch enhanced transformer encoder with attention-based feature
fusion. Specifically, a feature enhancement module is designed to enhance the
feature representation ability of the transformer encoder. The features
extracted from RGB and noise streams are fused effectively by the coordinate
attention-based fusion module at multiple scales. Extensive experimental
results verify that the proposed scheme achieves the state-of-the-art
generalization ability and robustness in various benchmark datasets. Code will
be public at https://github.com/multimediaFor/EITLNet.
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