Abstract
Nowadays, numerous corporations (such as Google, Baidu, etc.) require an efficient and effective search algorithm to crawl out the images with queried objects from databases. Moreover, privacy protection is a significant issue such that confidential images must be encrypted in corporations. Nevertheless, decrypting and then classifying millions of encrypted images becomes a heavy burden to computation. In this paper, we proposed an encrypted image classification framework based on multi-layer extreme learning machine that is able to directly classify encrypted images without decryption. Experiments were conducted on popular handwritten digits and letters databases. Results demonstrate that the proposed framework is secure, efficient and accurate for classifying encrypted images.
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References
Bardossy, A., & Duckstein, L. (1995). Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems (Vol. 8). Boca Raton: CRC press.
Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1–127.
Bost, R., Popa, R.A., Tu, S., & Goldwasser, S. (2014). Machine learning classification over encrypted data. Crypto ePrint Archive.
Cao, J., Huang, W., Zhao, T., Wang, J., & Wang, R. (2015). An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature. Multidimensional Systems and Signal Processing. doi:10.1007/s11045-015-0374-z.
Daemen, J., & Rijmen, V. (1999). AES Proposal: Rijndael. A Report, NIST, the Computer Security Resource Center (CSRC). http://www.science.upm.ro/~apetrescu/OLD/public_html/Tehnologia%20Informatiei/Securitatea%20informatiei/Laborator/AES/rijndael%20doc%20V2.pdf. Accessed 31 Mar 2016.
Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2–3), 131–163.
Graepel, T., Lauter, K., & Naehrig, M. (2013). Ml confidential: Machine learning on encrypted data. In: Information security and cryptology CICISC 2012, Lecture notes in computer science (Vol. 7839, pp. 1–21). Berlin: Springer.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489–501.
Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 513–529.
Ishibuchi, H., Nakashima, T., & Morisawa, T. (1999). Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets and Systems, 103(2), 223–238.
Katz, J., & Lindell, Y. (2014). Introduction to modern cryptography. Boca Raton: CRC Press.
LeCun, Y., Huang, F.J., & Bottou, L. (2004). Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition(CVPR) (vol 2, pp. II-97–104), IEEE.
Liou, C. Y., Cheng, W. C., Liou, J. W., & Liou, D. R. (2014). Autoencoder for words. Neurocomputing, 139, 84–96.
Liu, J., Jin, X., Dong, F., He, L., & Liu, H. (2016a). Fading channel modelling using single-hidden layer feedforward neural networks. Multidimensional Systems and Signal Processing. doi:10.1007/s11045-015-0380-1.
Liu, S., Feng, L., Liu, Y., Wu, J., Sun, M., & Wang, W. (2016b). Robust discriminative extreme learning machine for relevance feedback in image retrieval. Multidimensional Systems and Signal Processing. doi:10.1007/s11045-016-0386-3.
Ng, A. (2011). Sparse autoencoder. CS294A Lecture Notes, 72, 1–19. http://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf. Accessed 31 Mar 2016.
NIST-FIPS. (1977). Fips pub 46 (appendix a): Data encryption standard. Federal Information Processing Standards Publication.
NIST-FIPS. (2001). Announcing the advanced encryption standard (AES). Federal Information Processing Standards Publication, 197, 1C51.
Olshausen, B. A., et al. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607–609.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
Suykens, J., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11, 3371–3408.
Zaki, M., Ghalwash, A., & Elkouny, A. (1996). CNN: A speaker recognition system using a cascaded neural network. International Journal of Neural Systems, 7(02), 203–212.
Zhao, H., Wei, W., Cai, J., Lei, F., & Luo, J. (2015). Distributed compressed sensing for multi-sourced fusion and secure signal processing in private cloud. Multidimensional Systems and Signal Processing. doi:10.1007/s11045-015-0371-2.
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This research work is supported by the University of Macau Research Grant, Grant Numbers MYRG2014-00083-FST, MYRG2014-00178-FST, and Fundo para o Desenvolvimento das Ciencias e da Tecnologia, Grant Number FDCT/050/2015/A.
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Wang, W., Vong, CM., Yang, Y. et al. Encrypted image classification based on multilayer extreme learning machine. Multidim Syst Sign Process 28, 851–865 (2017). https://doi.org/10.1007/s11045-016-0408-1
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DOI: https://doi.org/10.1007/s11045-016-0408-1