2D Sine Logistic modulation map for image encryption

By Zhongyun Hua, Yicong Zhou, Chi-Man Pun, C. L. Philip Chen. Information Sciences, 2015

718 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b2148482ff4dc1de6d7fe93a9224d29a96fae8bd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Zhongyun Hua
2. Hejiao Huang
3. Yicong Zhou
4. Fan Jin
5. Binxuan Xu

In Statistical and Nonlinear Physics

1. Zhongyun Hua
2. Yicong Zhou

Citations in the corpus

• Image encryption using 2D Logistic-adjusted-Sine map (2016, methodology): “The 2D Sine Logistic modulation map (2D-SLMM) is defined as { xi+1 = α(sin(πyi) + β)xi(1 − xi), yi+1 = α(sin(πxi+1) + β)yi(1 − yi), where α and β are parameters, α ∈ [0, 1] and β is usually fixed as 3 [19].”
• 2D Logistic-Sine-coupling map for image encryption (2018, methodology): “The Logistic and Sine maps have many disadvantages such as simple behaviors and frail chaotic intervals, and these disadvantages may bring negative effects for some chaos-based applications [40] .”