Title: A self-adaptive quantum steganography algorithm based on QLSb modification in watermarked quantum image
Authors: Zhiguo Qu; Huangxing He; Wenjie Liu; Songya Ma
Addresses: Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu Province, China ' School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu Province, China ' Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu Province, China ' School of Mathematics and Statics, Henan University, Kaifeng 475004, Henan Province, China
Abstract: As one of important research branches of quantum information hiding, quantum steganography embeds secret information into quantum images for covert communication by integrating quantum secure communication technology and classical steganography. In this paper, based on the novel enhanced quantum representation (NEQR), a novel quantum steganography algorithm is proposed to transfer secret information by virtue of quantum watermarked image. In order to achieve this goal, the least significant qubit (LSQb) of quantum carrier image is replaced with the secret information by implementing quantum circuit. Compared with the previous quantum steganography algorithms, the communicating parties can recover the secret information tampered, meanwhile the tampers can be located effectively. In the experiment result, the peak signal-to-noise ratios (PSNRs) are calculated for different quantum watermarked images and quantum watermarks, which demonstrate the imperceptibility of the algorithm is good and the secret information embedded can be recovered by virtue of its self-adaptive mechanism.
Keywords: quantum steganography; quantum least significant bit; watermarked quantum carrier image.
International Journal of High Performance Computing and Networking, 2019 Vol.14 No.2, pp.121 - 129
Received: 09 Aug 2016
Accepted: 23 Feb 2017
Published online: 24 Jul 2019 *