Skip to main content
SpringerLink
Log in

Image Authentication by Single Target Region Detection

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11632))

Included in the following conference series:

Abstract

Digital image has been widely used in people’s daily life, and image authentication technology is more and more important. This paper proposes an image authentication method based on convolutional neural nets, which performed better compared with the VGG and Alex-Net. A single target region detection method under the attention model is proposed and it helps a lot in distinguish the source images and the derived images with watermarks or mosaic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wu, W.C.: Quantization-based image authentication scheme using QR error correction. Eurasip J. Image Video Process. 2017(1), 13 (2017)

    Article  Google Scholar 

  2. Shojanazeri, H., Wan, A.W.A., Ahmad, S.M.S., et al.: Authentication of images using Zernike moment watermarking. Multimedia Tools Appl. 76(1), 1–30 (2017)

    Article  Google Scholar 

  3. Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Computer Vision and Pattern Recognition, pp. 2818–2826. IEEE (2016)

    Google Scholar 

  4. Papandreou, G., Kokkinos, I., Savalle, P.A.: Untangling local and global deformations in deep convolutional networks for image classification and sliding window detection. Eprint Arxiv (2014)

    Google Scholar 

  5. Meng, R., Rice, S.G., Wang, J., Sun, X.: A fusion steganographic algorithm based on faster R-CNN. CMC Comput. Mater. Continua 55(1), 001–016 (2018)

    Google Scholar 

  6. Xiong, Z., Shen, Q., Wang, Y., Zhu, C.: Paragraph vector representation based on word to vector and CNN learning. CMC Comput. Mater. Continua 055(2), 213–227 (2018)

    Google Scholar 

Download references

Acknowledgement

This research has been supported by NSFC (61672495), Scientific Research Fund of Hunan Provincial Education Department (16A208), Project of Hunan Provincial Science and Technology Department (2017SK2405), and in part by the construct program of the key discipline in Hunan Province.

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Information Processing Ministry of Education, College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Huanrong Tang, Zhuosong Fu, Jianquan Ouyang & Yunhua Song

Authors
  1. Huanrong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuosong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianquan Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunhua Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianquan Ouyang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tang, H., Fu, Z., Ouyang, J., Song, Y. (2019). Image Authentication by Single Target Region Detection. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24274-9_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24273-2

  • Online ISBN: 978-3-030-24274-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation