Breaking a chaotic image encryption algorithm based on perceptron model

DOI10.1007/s11071-012-0329-yzbMath1254.68118arXiv1111.1410OpenAlexW2036600156WikidataQ123277624 ScholiaQ123277624MaRDI QIDQ1928985

Yu Zhang, Dan Zhang, Shi Shu, Qin Li, Cheng-Qing Li

Publication date: 4 January 2013

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.1410

zbMATH Keywords

chaosLorenz systemcryptanalysisknown-plaintext attackperceptron model

Mathematics Subject Classification ID

Cryptography (94A60) Data encryption (aspects in computer science) (68P25)

Related Items (7)

A novel efficient image encryption algorithm based on affine transformation combine with linear fractional transformation ⋮ Cryptanalysis of a parallel sub-image encryption method with high-dimensional chaos ⋮ Breaking a novel image encryption scheme based on improved hyperchaotic sequences ⋮ Cryptanalysis of a chaotic map-based password-authenticated key agreement protocol using smart cards ⋮ Hyperchaotic encryption based on multi-scroll piecewise linear systems ⋮ Cryptanalysis of a new image alternate encryption algorithm based on chaotic map ⋮ Analysis and improvement of a novel image fusion encryption algorithm based on DNA sequence operation and hyper-chaotic system

Uses Software

• NIST Statistical Test Suite

Cites Work

• Unnamed Item
• Cryptanalysis of a spatiotemporal chaotic image/video cryptosystem
• Cryptanalysis of two chaotic encryption schemes based on circular bit shift and XOR operations
• A speech encryption using fractional chaotic systems
• A symmetric image encryption scheme based on 3D chaotic cat maps
• A chaotic image encryption algorithm based on perceptron model
• BREAKING AN IMAGE ENCRYPTION ALGORITHM BASED ON CHAOS
• SOME BASIC CRYPTOGRAPHIC REQUIREMENTS FOR CHAOS-BASED CRYPTOSYSTEMS
• Communication Theory of Secrecy Systems*
• Symmetric Ciphers Based on Two-Dimensional Chaotic Maps
• ON THE DYNAMICAL DEGRADATION OF DIGITAL PIECEWISE LINEAR CHAOTIC MAPS
• Advances in Neural Networks – ISNN 2005

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