Computer Science ›› 2020, Vol. 47 ›› Issue (2): 276-280.doi: 10.11896/jsjkx.190100051

• Information Security • Previous Articles     Next Articles

Cryptanalysis of Medical Image Encryption Algorithm Using High-speed Scrambling and Pixel Adaptive Diffusion

YU Feng,GONG Xin-hui,WANG Shi-hong   

  1. (School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China)
  • Received:2019-01-07 Online:2020-02-15 Published:2020-03-18
  • About author:YU Feng,born in 1993,postgraduate.His main research interests include chaotic encryption;WANG Shi-hong,born in 1966,Ph.D,professor.Her main research interests include chaotic encryption.

Abstract: Security is essential and important for every image encryption algorithm.Medical image encryption is a means to protect patients’ privacy.Analyzing the security of medical image encryption algorithm is very meaningful for the design of medical image encryption algorithm,enhancing the security of algorithm and promoting the application of medical image encryption algorithm.Recently,Hua et al.proposed a medical image encryption algorithm using high-speed scrambling and pixel adaptive diffusion.The key operation of the scheme is insertion of a random sequence around an image,then the random values are dispersed to the whole image by scrambling,finally,the whole image is scrambled by diffusion.Because different random values are generated in each encryption,even for one unchanged image,the cipher-image is different in every encryption such that Hua et al’s scheme is similar to one time one pad system.In this paper,the security of the algorithm was analyzed by differential cryptanalysis and chosen ciphertext attack in detail.The decryption process is analyzed theoretically by differential cryptanalysis and linear relationship is constructed between plain-images and cipher-images.Based on the linear relationship,a codebook is established,and the codebook attack breaks Hua et al’s algorithm.The size of the codebook is determined by the size of the cipher-image.If the size of the cipher-image is,the constructed codebook contains pairs of plain-image/cipher-image.The experimental results verify the theoretical analysis.To improve the security of Hua et al’s algorithm and to resist the differential cryptanalysis,an improved scheme was proposed.In the improved scheme,plaintext-related permutation matrices are introduced.The simulation and statistical results show that the improved scheme not only inherits the advantages of the original algorithm,but also resist the differentialcryptanalysis and the codebook attack.

Key words: Chaotic encryption, Codebook attack, Differential cryptanalysis, Image encryption, Medical image

CLC Number: 

  • TP391
[1]ROBERT A M.On the derivation of a “chaotic” encryption algorithm[J].Cryptologia,1989,13(1):29-42.
[2]FRIDRICH J.Image encryption based on chaotic maps[C]∥ IEEE International Conference on Systems.IEEE,1997.
[3]CHAI X,ZHENG X,GAN Z,et al.An image encryption algorithm based on chaotic system and compressive sensing[J].Signal Processing,2018,148:124-144.
[4]CHEN J,ZHU Z L,ZHANG L B,et al.Exploiting self-adaptive permutation-diffusion and DNA random encoding for secure and efficient image encryption[J].Signal Processing,2018,142:340-353.
[5]ZHANG L Y,LIU Y,WONG K W,et al.On the security of a class of diffusion mechanisms for image encryption[J].IEEE Transactions on Cybernetics,2017,PP(99):1-13.
[6]QIWEN R,LING W,JING M,et al.A quantum color image encryption scheme based on coupled hyper-chaotic Lorenz system with three impulse injections[J].Quantum Information Proces-sing,2018,17(8):188.
[7]CAO C,SUN K,LIU W.A novel bit-level image encryption algorithm based on 2D-LICM hyperchaotic map[J].Signal Processing,2017,143:122-133.
[8]CHEN J,ZHU Z L,ZHANG L B,et al.Exploiting self-adaptive permutation-diffusion and DNA random encoding for secure and efficient image encryption[J].Signal Processing,2018,142:340-353.
[9]LI S,LI C,CHEN G,et al.A general quantitative cryptanalysis of permutation-only multimedia ciphers against plaintext attacks[J].Signal Processing:Image Communication,2008,23(3):212-223.
[10]LI C Q,LIU Y S,XIE T,et al.Breaking a novel image encryption scheme based on improved hyperchaotic sequences[J].Nonlinear Dynamics,2013,73(3):2083-2089.
[11]SOLAK E,COKAL C,YILDID O T,et al.Cryptanalysis of fridrich’s chaotic image encryption[J].International Journal of Bifurcation & Chaos,2010,20:1405-1413.
[12]FU C,MENG W,ZHAN Y,et al.An efficient and secure medical image protection scheme based on chaotic maps[J].Compu-ters in Biology & Medicine,2013,43(8):1000-1010.
[13]ZHOU G,ZHANG D,LIU Y,et al.A novel image encryption algorithm based on chaos and Line map[J].Neurocomputing,2015,169:150-157.
[14]CHEN L,WANG S H.Differential cryptanalysis of a medical image cryptosystem with multiple rounds[M].British:Pergamon Press,2015.
[15]CHEN L,MA B,ZHAO X,et al.Differential cryptanalysis of a novel image encryption algorithm based on chaos and Line map[J].Nonlinear Dynamics,2016,87(3):1-11.
[16]HUA Z Y,YI S,ZHOU Y C.Medical image encryption using high-speed scrambling and pixel adaptive diffusion[J].Signal Processing,2017,144:134-144.
[17]RUKHIN A L,SOTO J,NECHVATAL J R,et al.SP 800-22 Rev.1a.A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications[J].Applied Physics Letters,2010,22(7):1645-1796.
[18]PARESCHI F,ROVATTI R,SETTI G.On Statistical Tests for Randomness Included in the NIST SP800-22 Test Suite and Based on the Binomial Distribution[J].IEEE Transactions on Information Forensics and Security,2012,7(2):491-505.
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