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Image Tampering Localization Using Unified Two-Stream Features Enhanced with Channel and Spatial Attention

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

Image tampering localization has attracted much attention in recent years. To differentiate between tampered and pristine image regions, many methods have increasingly leveraged the powerful feature learning ability of deep neural networks. Most of these methods operate on either spatial image domain directly or residual image domain constructed with high-pass filtering, while some take inputs from both domains and fuse the features just before making decisions. Though they have achieved promising performance, the gain of integrating feature representations of different domains is overlooked. In this paper, we show that learning a unified feature set is beneficial for tampering localization. In the proposed method, low-level features are firstly extracted from two input streams: one is a spatial image, and the other is a high-pass filtered residual image. The features are then separately enhanced with channel attention and spatial attention, and are subsequently subjected to an early-fusion to form a unified feature representation. The unified features play an important role under an adapted Mask R-CNN framework, achieving more accurate pixel-level tampering localization. Experimental results on five tampered image datasets have shown the effectiveness of the proposed method. The implementation is available at https://github.com/media-sec-lab/AEUF-Net.

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Notes

  1. 1.

    The results of conventional methods are all underlined, since these methods are unsupervised ones.

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Acknowledgement

This work was supported in part by NSFC (Grant 61802262 and Grant 61872244), Guangdong Basic and Applied Basic Research Foundation (Grant 2019B151502001), Shenzhen R&D Program (Grant JCYJ20200109105008228), and in part by the Alibaba Group through Alibaba Innovative Research (AIR) Program.

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Authors and Affiliations

  1. Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, 518060, China

    Haodong Li, Xiaoming Chen, Peiyu Zhuang & Bin Li

  2. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China

    Haodong Li, Xiaoming Chen, Peiyu Zhuang & Bin Li

Authors
  1. Haodong Li
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  2. Xiaoming Chen
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  3. Peiyu Zhuang
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  4. Bin Li
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Corresponding author

Correspondence to Bin Li .

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Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Li, H., Chen, X., Zhuang, P., Li, B. (2021). Image Tampering Localization Using Unified Two-Stream Features Enhanced with Channel and Spatial Attention. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_50

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_50

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  • Online ISBN: 978-3-030-88007-1

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