Effective Image Tampering Localization via Semantic Segmentation Network
- URL: http://arxiv.org/abs/2208.13739v2
- Date: Tue, 30 Aug 2022 14:19:27 GMT
- Title: Effective Image Tampering Localization via Semantic Segmentation Network
- Authors: Haochen Zhu, Gang Cao, Mo Zhao
- Abstract summary: Existing image forensic methods still face challenges of low accuracy and robustness.
We propose an effective image tampering localization scheme based on deep semantic segmentation network.
- Score: 0.4297070083645049
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the widespread use of powerful image editing tools, image tampering
becomes easy and realistic. Existing image forensic methods still face
challenges of low accuracy and robustness. Note that the tampered regions are
typically semantic objects, in this letter we propose an effective image
tampering localization scheme based on deep semantic segmentation network.
ConvNeXt network is used as an encoder to learn better feature representation.
The multi-scale features are then fused by Upernet decoder for achieving better
locating capability. Combined loss and effective data augmentation are adopted
to ensure effective model training. Extensive experimental results confirm that
localization performance of our proposed scheme outperforms other
state-of-the-art ones.
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