Abstract
For the purpose of information transmission security, image scrambling is to encrypt the image by changing the image pixel values and pixel positions. Based on the asymmetric encryption, we propose a model of Image Scrambling Adversarial Autoencoder. Firstly, we describe an encoder-decoder framework to imitate the procedure of image scramble, descramble and the key generation. Secondly, we employ the generator network of CycleGAN as the encoder and decoder structure of our method to transfer the secret image to totally meaningless image and reconstruct it. Thirdly, the parameters of the encoder and decoder can be regarded as the public key and private key. Then, the patchGANs discriminator is used to distinguish encoded images and evenly distributed noise by image blocks. Moreover, we combine the encode-then-decode loss function with the adversarial loss function by an adjustable parameter in order to make the model training results more stable. Experiments show that our method can accomplish automatic image scrambling in ten different scenes which include Africa people and villages, beach, buildings, buses, dinosaurs, elephants, flowers, horses, mountains and glaciers, food. Compared with 3D Arnold transformation and CycleGAN, scrambled pixels by our method are more evenly distributed intuitively. What is more, extensive experiments show that the proposed method can address security requirements partly and achieve a good encryption efficiency. In addition, contrast experimental results show that the combination of the encode-then-decode loss function and the adversarial loss function is essential to achieve the ideal results of image scrambling and restoration.
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References
M AprilPyone, W Sirichotedumrong, H. Kiya (2019) Adversarial test on learnable image encryption in 2019 IEEE 8th global conference on consumer electronics (GCCE), pp 667-669. IEEE, Osaka, https://doi.org/10.1109/GCCE46687.2019.9015447
Awais Y, Hanan A, Musheer A, Muhammad D, Abdul R (2020) Comparison of pre and post-action of a finite Abelian group over certain nonlinear schemes. IEEE Access 8:39781–39792
Baek J, Lee B, Kim K (2000) Secure length-saving ElGamal encryption under the computational Diffie-Hellman assumption. In: Proceedings of the 5th Australasian conference on information security and privacy (ACISP '00). Springer-Verlag, Berlin, Heidelberg, pp 49–58
Battisti F, Cancellaro M, Boato G, Carli M, Neri A (2009) Joint watermarking and encryption of color images in the Fibonacci-Haar domain. EURASIP J Adv Signal Process 2009:43. https://doi.org/10.1155/2009/938515
Belazi A, El-Latif AAA, Belghith S (2016) A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process 128:155–170
Chen G, Mao Y, Chui CK (2004) A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos, Solitons Fractals 21(3):749–761
Chen J, Li XW, Wang QH (2019) Deep learning for improving the robustness of image encryption. IEEE Access 7:181083–181091
Daras G, Odena A, Zhang H, Dimakis AG (2020) Your local GAN: designing two dimensional local attention mechanisms for generative models. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Seattle, pp 14519–14527
Ding Y, Wu GZ, Chen DJ, Zhang N, Gong L, Cao M, Qin Z (2020) DeepEDN: a deep learning-based image encryption and decryption network for internet of medical things. IEEE Internet Things J 8:1504–1518. https://doi.org/10.1109/JIOT.2020.3012452
Dong J, Wu G, Yang T, Li Y (2018) The improved image scrambling algorithm for the wireless image transmission systems of UAVs. Sensors (Basel, Switzerland) 18(10):3430. https://doi.org/10.3390/s18103430
Feng L, Wu J, Liu S, Zhang H (2015) Global correlation descriptor: a novel image representation for image retrieval. J Vis Commun Image Represent 33:104–114
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems - volume 2 (NIPS'14). MIT Press, Cambridge, pp 2672–2680
Gu J, Shen Y, Zhou B (2020) Image processing using multi-code GAN prior. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Seattle, pp 3009–3018
Guo T, Xu C, Huang J, Wang Y, Shi B, Xu C, Tao D (2020) On positive-unlabeled classification in GAN. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Seattle, pp 8382–8390
Hamza YA (2019) Highly secure image steganography approach using Arnold's cat map and maximum image entropy. In: Proceedings of the international conference on information and communication technology (ICICT '19). ACM, New York, pp 134–138
He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Boston, pp 5353–5360
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, pp 770–778
Heidari S, Vafaei M, Houshmand M, Tabatabaey-Mashadi N (2019) A dual quantum image scrambling method. Quantum Inf Process 18(1):9. https://doi.org/10.1007/s11128-018-2122-4
Hu F, Pu CJ, Gao HW, Tang MZ, Li L (2016) An image compression and encryption scheme based on deep learning arXiv: 1608.05001
Hua Z, Zhu Z, Yi S, Zhang Z, Huang H (2021) Cross-plane colour image encryption using a two-dimensional logistic tent modular map. Inf Sci 546:1063–1083
Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Honolulu, pp 5967–5976
Kandar S, Chaudhuri D, Bhattacharjee A, Dhara BC (2019) Image encryption using sequence generated by cyclic group. J Inform Secur Appl 44:117–129. https://doi.org/10.3969/j.issn.1006-8961.2004.10.013
Kang XJ, Ran T (2019) Color image encryption using pixel scrambling operator and reality-preserving MPFRHT. IEEE Trans Circuits Syst Video Technol 29(7):1919–1932
Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th international conference on machine learning - volume 70 (ICML'17). JMLR.org, Sydney, pp 1857–1865
Kingma D, Ba J (2014) Adam: A method for stochastic optimization arXiv: 1412.6980
Kingma D P, Welling M (2014) Auto-encoding variational bayes. In 2nd international conference on learning representations, ICLR. arXiv: 1312.6114
Kocher PC (1996) Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In: Proceedings of the 16th annual international cryptology conference on advances in cryptology, vol 1109. Springer, Berlin
Koki M, Masayuki T, Masaki O, Tetsuji O (2020) Block-wise Scrambled Image Recognition Using Adaptation Network arXiv: 2001.07761
Liu H, Wang X (2010) Color image encryption based on one-time keys and robust chaotic maps. Comput Mathematics Appl 59(10):3320–3327
Liu H, Wang X (2011) Color image encryption using spatial bit-level permutation and high-dimension chaotic system. Opt Commun 284(16–17):3895–3903
Liu H, Wang X, Kadir A (2012) Image encryption using DNA complementary rule and chaotic maps. Appl Soft Comput 12(5):1457–1466. https://doi.org/10.1109/JIOT.2020.3012452
Liu Z, Dai J, Sun X, Liu S (2010) Color image encryption by using the rotation of color vector in Hartley transform domains. Optics Lasers Eng 48(7–8):800–805
Luma A, Raufi B, Zenuni X (2012) Asymmetric encryption / decryption with Pentor and ultra Pentor operators. J Sci Technol 2:9–12
Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial Autoencoders arXiv: 1511.05644
Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP (2017) Least squares generative adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV). Venice, Italy, pp 2813–2821
Martin A, Soumith C, Léon B (2017) Wasserstein GAN arXiv: 1701.07875
Miller VS (1985) Use of elliptic curves in cryptography. Advances in cryptology. In: Advances in cryptology (CRYPTO '85). Springer-Verlag, Berlin, pp 417–426
Parvin Z, Seyedarabi H, Shamsi M (2016) A new secure and sensitive image encryption scheme based on new substitution with chaotic function. Multimed Tools Appl 75(17):10631–10648
Pellegrini A, Bertacco V, Austin T (2010) Fault-based attack of RSA authentication. In: Proceedings of the conference on design, automation and test in Europe (DATE '10). IEEE, Dresden, pp 855–860
Qi D, Zou J, Han X (2000) A new class of scrambling transformation and its application in the image information covering. Sci China Series E: Technol Sci 43:304–312. https://doi.org/10.1007/BF02916835
Qin YY, Zhang CN, Liang R, Chen MR (2019) Research on face image encryption based on deep learning. IOP Conference Series Earth Environ Sci 252(5):052007
Tanaka M (2018) Learnable image encryption. In: 2018 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, Taichung, pp 1–2
Taneja N, Raman B, Gupta I (2011) Selective image encryption in fractional wavelet domain. AEU - Intl J Electronics Commun 65(4):338–344
Taneja Nidhi, Raman Balasubramanian, Gupta Indra (2011) Chaos based partial encryption of SPIHT compressed images. Intl J Wavelets, Multiresolution Inform Process. 9(2): 317
Tang ZJ (2017) Image scrambling encryption algorithm based on chaotic mapping. J Changsha Aeronautical Vocational Tech College 17(02):90–92. (in Chinese. https://doi.org/10.13829/j.cnki.issn.16719654.2017.02.022
Tang Z, Zhang X, Lan W (2015) Efficient image encryption with block shuffling and chaotic map. Multimed Tools Appl 74:5429–5448
Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: The missing ingredient for fast stylization. arXiv:1607.08022, 2016.
Uppu R, Wolterink T A W, Goorden S A, Chen B. C, et al. (2018). Asymmetric cryptography with physical unclonable keys. Quantum Sci Technol 4: 045011
Wang X, Gao S (2020) Image encryption algorithm based on the matrix semi-tensor product with a compound secret key produced by a Boolean network. Information Ences 539:195–214
Wang X, Gao S (2020) Image encryption algorithm for synchronously updating Boolean networks based on matrix semi-tensor product theory. Information Ences 507:16–36
Xu W, Luo Y, Li T, Wang H, Shi Y (2017) Multiple-image hiding by using single-shot ptychography in transform domain. IEEE Photonics J 9(3):1–10
Wang X, Feng L, Zhao H (2019) Fast image encryption algorithm based on parallel computing system. Inf Sci 486:340–358
Xian Y, Wang X (2021) Fractal sorting matrix and its application on chaotic image encryption. Information Ences 547:1154–1169
Xiong G, Zheng S, Wang J, Cai Z, Qi D (2018) Local negative base transform and image scrambling. Math Probl Eng 2018:8087958
Wang XY, Yang L, Liu R, Kadir A (2010) A chaotic image encryption algorithm based on perceptron model. Nonlinear Dynamics 62(3):615–621
Zhu J, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks in 2017 IEEE international conference on computer vision (ICCV). pp 2242-2251. IEEE, Venice, Italy. https://doi.org/10.1109/ICCV.2017.244
Wu C, Wang Y, Chen Y, Wang J, Wang QH (2018) Asymmetric encryption of multiple-image based on compressed sensing and phase-truncation in cylindrical diffraction domain. Opt Commun 431:203–209
Yuan X, Huang B, Wang Y, Yang C, Gui W (2018) Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE. IEEE Trans Indust Informatics 14(7):3235–3243
Yi Z, Zhang H, Tan P, Gong M (2017) DualGAN: unsupervised dual learning for image-to-image translation. In: 2017 IEEE international conference on computer vision (ICCV). IEEE, Venice, pp 2868–2876
Wang T, Liu M, Zhu J, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional GANs in 2018 IEEE/CVF conference on computer vision and pattern recognition. IEEE, Salt Lake City, pp 8798–8807
Wang XY, Zhang YQ, Bao XM (2015) A novel chaotic image encryption scheme using DNA sequence operations. Opt Lasers Eng 73:53–61
Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: The Thrity-seventh Asilomar conference on signals, systems & computers. IEEE, Pacific Grove, pp 1398–1402
Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: The Thrity-seventh Asilomar conference on signals, systems & computers. IEEE, Pacific Grove, pp 1398–1402
Wei XP, Guo L, Zhang Q, Zhang JX, Lian SG (2012) A novel color image encryption algorithm based on DNA sequence operation and hyper-chaotic system. J Syst Softw 85(2):290–299
Xinguang S, Luo H (2004) Digital image scrambling based on S-box. J Image Graphics 10:79–83 (in Chinese). https://doi.org/10.3969/j.issn.1006-8961.2004.10.013
Zhang H, Goodfellow IJ, Metaxas D, Odena A. (2018) Self-attention generative adversarial networks. arXiv:1805.08318, May 2018.
Zhang Y (2018) The unified image encryption algorithm based on chaos and cubic s-box. Inf Sci 450:361–377
Zhang YQ, Wang XY (2015) A new image encryption algorithm based on non-adjacent coupled map lattices. Appl Soft Comput 26:10–20
Zhang YQ, Wang XY (2014) A symmetric image encryption algorithm based on mixed linear–nonlinear coupled map lattice. Information Ences 273:329–351
Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Honolulu, pp 4352–4360
Acknowledgments
This study is supported in part by the National Key Research and Development Project of China under Grant 2017YFB1402100, in part by the Natural Science Foundation of Xizang Autonomous Region of China under Grant XZ2018ZR G-64.
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Bao, Z., Xue, R. & Jin, Y. Image scrambling adversarial autoencoder based on the asymmetric encryption. Multimed Tools Appl 80, 28265–28301 (2021). https://doi.org/10.1007/s11042-021-11043-3
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DOI: https://doi.org/10.1007/s11042-021-11043-3