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Breaking an Image Encryption Algorithm Based on DNA Encoding and Spatiotemporal Chaos.
Wen, Heping; Yu, Simin; Lü, Jinhu.
Afiliação
  • Wen H; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Yu S; School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Lü J; School of Automation Science and Electrical Engineering, State Key Laboratory of Software Development Environment, and Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China.
Entropy (Basel) ; 21(3)2019 Mar 05.
Article em En | MEDLINE | ID: mdl-33266960
ABSTRACT
Recently, an image encryption algorithm based on DNA encoding and spatiotemporal chaos (IEA-DESC) was proposed. In IEA-DESC, pixel diffusion, DNA encoding, DNA-base permutation and DNA decoding are performed successively to generate cipher-images from the plain-images. Some security analyses and simulation results are given to prove that it can withstand various common attacks. However, in this paper, it is found that IEA-DESC has some inherent security defects as follows (1) the pixel diffusion is invalid for attackers from the perspective of cryptanalysis; (2) the combination of DNA encoding and DNA decoding is equivalent to bitwise complement; (3) the DNA-base permutation is actually a fixed position shuffling operation for quaternary elements, which has been proved to be insecure. In summary, IEA-DESC is essentially a combination of a fixed DNA-base position permutation and bitwise complement. Therefore, IEA-DESC can be equivalently represented as simplified form, and its security solely depends on the equivalent secret key. So the equivalent secret key of IEA-DESC can be recovered using chosen-plaintext attack and chosen-ciphertext attack, respectively. Theoretical analysis and experimental results show that the two attack methods are both effective and efficient.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article