Grayscale Image Authentication using Neural Hashing

Abstract

Many different approaches for neural network based hash functions have been proposed. Statistical analysis must correlate security of them. This paper proposes novel neural hashing approach for gray scale image authentication. The suggested system is rapid, robust, useful and secure. Proposed hash function generates hash values using neural network one-way property and non-linear techniques. As a result security and performance analysis are performed and satisfying results are achieved. These features are dominant reasons for preferring against traditional ones.

Keywords

• Image Authentication,Cryptology,Hash Function,Statistical and Security Analysis

References

1. Arvandi, M., Wu, S., Sadeghian, A., Melek, W. W., & Woungang, I. (2006). Symmetric Cipher Design Using Recurrent Neural Networks. International Joint Conference on Neural Networks, pp.2039–2046.
2. Diffie, W. & Hellman, M.E. (1977). Special Feature Exhaustive Cryptanalysis of the NBS Data Encryption Standard. Computer, 10(6), pp.74–84.
3. Huang, Z. (2011). A more secure parallel keyed hash function based on chaotic neural network. Communications in Nonlinear Science and Numerical Simulation, 16(8), pp.3245–3256.
4. Lian, S., Liu, Z., Ren, Z., & Wang, H. (2006). Hash function based on chaotic neural networks. 2006 IEEE International Symposium on Circuits and Systems, p.4.
5. Lian, S., Sun, J., & Wang, Z. (2007). One-way Hash Function Based on Neural Network. CoRR.
6. Monga, V., Evans, B.L. & Member, S. (2006). Perceptual Image Hashing Via Feature Points : Performance Evaluation and Tradeoffs. Ieee Transactions On Image Processing, 15(11), pp.3453–3466.
7. Rivest, R. (1992). The MD5 Message Digest Algorithm. MIT Laboratory for Computer Science and RSA Data Security, Inc.
8. Sağıroğlu, S. & Özkaya, N. (2007). Neural Solutions for Information Security. Journal of Polytechnic, 10(1), pp.21–25.

9. Seo, J. S., Haitsma, J., Kalker, T., & Yoo, C. D. (2004). A robust image fingerprinting system using the Radon transform. Signal Processing: Image Communication, 19, pp.325–339.
10. Soyalıç, S. (2005). Cryptographic Hash Functions And Its Applications. Erciyes University Natural and Applied Sciences M. S. Thesis.
11. Sumangala, G. (2011). Performance Analayis of Sha-2 Algorithm With And Without using Artificial Neural Networks. World Journal of Science and Technology, 1(12), pp.12–20.
12. Swaminathan, A., Mao, Y., & Wu, M. (2006). Robust and Secure Image Hashing. Ieee Transactions On Information Forensics And Security, 1(2), pp.215–230.
13. Vanstone, S.A., A.J.Menezes., & P.C.Oorshot. (1996). Handbook of Applied Cryptography, Boca Raton: CRC Press.
14. Yayık, A., & Kutlu, Y. (2015) Neural Network Based Cryptography. Neural Network World, 24(2), pp.177–192.
15. Yayık, A., & Kutlu, Y. (2013). Yapay Sinir Ağları ile Kriptoloji Uygulaması. Mustafa Kemal Üniversitesi.
16. Zou, A. & Xiao, X. (2009). An Asynchronous Encryption Arithmetic Based on Laguerre Chaotic Neural Networks. 2009 WRI Global Congress on Intelligent Systems, pp.36–39.