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A Noise Convolution Network for Tampering Detection

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14263))

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Abstract

The vulnerability of digital images to tampering is an ongoing information security issue in the multimedia field. Thus, identifying tampered digital images and locating the tampered regions in the images can help improve the security of information dissemination. A deep fusion neural network named NC-Net is designed in this paper, introducing pattern noise as assistance to fully exploit the tampered features present on the tampered image. The incorporation of noise texture information enabled NC-Net to acquire deeper tampered image features during the training phase. The extracted noise is incorporated as a crucial component within the convolutional structure of the model, serving as a potent activation signal for the tampered region. The performance of NC-Net is confirmed through relevant experiments on publicly available tampered datasets, and outstanding results are achieved in comparison to other methods.

This work was supported by the Science and Technology Development Fund of Macau SAR (Grant number 0045/2022/A), and the Research project of the Macao Polytechnic University (Project No. RP/FCA-12/2022).

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Acknowledgment

This work was supported by the Science and Technology Development Fund of Macau SAR (grant number 0045/2022/A), and the Research project of the Macao Polytechnic University (Project No. RP/FCA-12/2022).

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Authors and Affiliations

  1. Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR, China

    Zhiyao Xie, Xiaochen Yuan & Chan-Tong Lam

  2. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Guoheng Huang

Authors
  1. Zhiyao Xie
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  2. Xiaochen Yuan
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  3. Chan-Tong Lam
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  4. Guoheng Huang
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Corresponding author

Correspondence to Xiaochen Yuan .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Xie, Z., Yuan, X., Lam, CT., Huang, G. (2023). A Noise Convolution Network for Tampering Detection. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14263. Springer, Cham. https://doi.org/10.1007/978-3-031-44204-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-44204-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44203-2

  • Online ISBN: 978-3-031-44204-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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