An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Xin Zhong, Pei Chi Huang, Spyridon Mastorakis, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.

Original languageEnglish (US)
Article number9133124
Pages (from-to)1951-1961
Number of pages11
JournalIEEE Transactions on Multimedia
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Image watermarking
  • automation
  • convolutional neural networks
  • deep learning
  • robustness


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