Nonlinear QR code based optical image encryption using spiral phase transform, equal modulus decomposition and singular value decomposition

Ravi Kumar, Basanta Bhaduri, Naveen K. Nishchal

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

In this study, we propose a quick response (QR) code based nonlinear optical image encryption technique using spiral phase transform (SPT), equal modulus decomposition (EMD) and singular value decomposition (SVD). First, the primary image is converted into a QR code and then multiplied with a spiral phase mask (SPM). Next, the product is spiral phase transformed with particular spiral phase function, and further, the EMD is performed on the output of SPT, which results into two complex images, Z 1 and Z 2. Among these, Z 1 is further Fresnel propagated with distance d, and Z 2 is reserved as a decryption key. Afterwards, SVD is performed on Fresnel propagated output to get three decomposed matrices i.e. one diagonal matrix and two unitary matrices. The two unitary matrices are modulated with two different SPMs and then, the inverse SVD is performed using the diagonal matrix and modulated unitary matrices to get the final encrypted image. Numerical simulation results confirm the validity and effectiveness of the proposed technique. The proposed technique is robust against noise attack, specific attack, and brutal force attack. Simulation results are presented in support of the proposed idea.

Original languageEnglish
Article number015701
JournalJournal of Optics (United Kingdom)
Volume20
Issue number1
DOIs
StatePublished - 1 Jan 2018
Externally publishedYes

Keywords

  • QR code
  • optical image encryption
  • singular value decomposition
  • spiral phase transform

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

Fingerprint

Dive into the research topics of 'Nonlinear QR code based optical image encryption using spiral phase transform, equal modulus decomposition and singular value decomposition'. Together they form a unique fingerprint.

Cite this