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Hybrid features and semantic reinforcement network for image forgery detection

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Abstract

Image forgery detection focuses more on tampering regions than image content of semantic segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient semantic information causes low efficiency of forgery detection. To address these issues, we propose a hybrid features and semantic reinforcement network (HFSRNet) for image forgery detection, which is an encoding and decoding based network. Specifically, long-short term memory with resampling features has been applied to capture traces from the image patches for finding manipulating artifacts. Consolidated features extracted from rotating residual units are further leveraged to amplify the discrepancy between un-tampered and tampered regions. We then hybridize features from them through a concatenation to further incorporate spatial co-occurrence of these two modalities. In addition, for achieving the semantic consistency between two same level features associated by across layers, semantic reinforcement is implemented on the decoding stage. HFSRNet is an end-to-end architecture that handles multiple types of image forgery including copy-move, splicing, removal. Experiments on three standard image manipulation datasets (NIST16, COVERAGE and CASIA) demonstrate that HFSRNet obtains state-of-the-art performance compared to existing models and baselines.

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Acknowledgements

This research is supported by the National Key Research and Development Program of China (2018YFB0804203), Regional Joint Fund of NSFC (U19A2057), the National Natural Science Foundation of China (61672259, 61876070), and the Jilin Province Science and Technology Development Plan Project(20190303134SF, 20180201064SF).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Haipeng Chen & Zenan Shi

  2. College of Software, Jilin University, Changchun, 130012, China

    Chaoqun Chang

  3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Haipeng Chen, Chaoqun Chang & Zenan Shi

  4. Public Computer Education and Research Center, Jilin University, Changchun, 130012, China

    Yingda Lyu

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  1. Haipeng Chen
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  2. Chaoqun Chang
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Correspondence to Yingda Lyu.

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Chen, H., Chang, C., Shi, Z. et al. Hybrid features and semantic reinforcement network for image forgery detection. Multimedia Systems 28, 363–374 (2022). https://doi.org/10.1007/s00530-021-00801-w

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