Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

04-06-2020 | Issue 4/2020

Cognitive Computation 4/2020

An Effective Semi-fragile Watermarking Method for Image Authentication Based on Lifting Wavelet Transform and Feed-Forward Neural Network

Journal:
Cognitive Computation > Issue 4/2020
Authors:
Behrouz Bolourian Haghighi, Amir Hossein Taherinia, Reza Monsefi
Important notes

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Digital watermarking is a significant issue in the field of information security and avoiding the misuse of images in the world of Internet and communication. This paper proposes a novel watermarking method for tamper detection and recovery using semi-fragile data hiding, based on lifting wavelet transform (LWT) and feed-forward neural network (FNN). In this work, first, the host image is decomposed up to one level using LWT, and the discrete cosine transform (DCT) is applied to each 2×2 blocks of diagonal details. Next, a random binary sequence is embedded in each block as the watermark by correlating DC coefficients. In the authentication stage, first, the geometry is analyzed by using speeded up robust features (SURF) algorithm and extract watermark bits by using FNN. Afterward, logical exclusive or operation between original and extracted watermark is applied to detect tampered region. Eventually, in the recovery stage, tampered regions are recovered using the inverse halftoning technique. The performance and efficiency of the method and its robustness against various geometric, non-geometric, and hybrid attacks are reported. From the experimental results, it can be seen that the proposed method is superior in terms of robustness and quality of the watermarked and recovered images, respectively, compared to the state-of-the-art methods. Besides, imperceptibility has been improved by using different correlation steps as the gain factor for flat (smooth) and texture (rough) blocks. Based on the advantages exhibited, the proposed method outperforms the related works, in terms of superiority, efficiency, and effectiveness for tamper detection and recovery-based applications.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 4/2020

Cognitive Computation 4/2020 Go to the issue

Premium Partner

    Image Credits