Exploiting one-dimensional exponential Chebyshev chaotic map and matching embedding for visually meaningful image encryption
Guoqiang Long,
Xiuli Chai,
Zhihua Gan,
Donghua Jiang,
Xin He and
Mengge Sun
Chaos, Solitons & Fractals, 2023, vol. 176, issue C
Abstract:
As a result of their prominent initial value sensitivity, pseudo-randomness, and unpredictability nature, chaotic maps are widely used in multimedia security. However, the majority of chaotic maps now in use have issues like narrow parameter scope, poor traversability, and insufficient chaotic performance. To address these issues, a one-dimensional exponential Chebyshev (1-DEC) chaotic map is constructed in this paper which performs satisfactorily. Through the use of a bifurcation diagram, Lyapunov exponents, sample entropy, and permutation entropy, the chaotic dynamics of the 1-DEC are thoroughly examined. Subsequently, a visually meaningful image encryption (VMIE) scheme combined with matching embedding based on 1-DEC was presented. Specifically, a cyclic shift confusion method across rows and columns (CSCARC) is proposed to eliminate the correlation of adjacent pixels. Then, an adaptive embedding method based on image energy and dynamic matching (AEIEDM) is proposed to fuse the privacy information into carrier images. In addition, secret image share and authentication information is introduced in this scheme, which improves disaster-tolerance performance and implements the distributed management and storage of data. Subscribers have the capabilities of authentication, management, and data integrity checking. Ultimately, the simulation results and analysis demonstrate that the proposed scheme possesses satisfactory performance in terms of security and robustness, and the visual quality of the steganographic images was above 50.9 dB.
Keywords: Chaotic map; Image encryption; Adaptive embedding (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010123
DOI: 10.1016/j.chaos.2023.114111
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