General Regression Neural Network Approach for Image Transformation Based Hybrid Graphical Password Authentication System

  • Authors

    • P. Baby Maruthi
    • Research Scholar
    • SPMVV Tirupathi.
    https://doi.org/10.14419/ijet.v7i3.24.22792
  • Image Transformation, Feature Extraction, Graphical Passwords, General Regression Neural Network
  • Abstract

    In this digital generation, computer, and information security plays a prominent role for both individuals and business organizations. In this interconnected business environment, information is the most valuable asset and it is of utmost importance to both individuals and organizations. The task of protecting information can be achieved through authentication. Today, textual password authentication is with username and password combination commonly used for many web applications. But textual passwords are the weakest form of authentication and it is easily guessed by the attacker by applying the various techniques such as brute force, dictionary attack, etc. To provide security from vulnerable attacks, graphical passwords are another alternative authentication mechanism for replacing the textual passwords.  This paper proposes image transformation based hybrid graphical password authentication model utilizes general regression neural network model and feature extraction methods for user identification. Three types of image transformations such as normal image, mirror image and shift image are considered to enhance security. In this paper, three types of feature extraction techniques such as SURF, LBP and HOG are considered for extracting image features. The performance of the proposed model is analysed, in terms of usability, security and storage space analysis and the results proved that the proposed system is resistant against various attacks like brute force, dictionary attack, shoulder surfing etc.

     

  • References

    1. [1] P., Baby Maruthi and Dr.K., Sandhya Rani, Image Transformation Based Hybrid Graphical Password Authentication System (February 7, 2018). 2018 IADS International Conference on Computing, Communications & Data Engineering (CCODE) 7-8 February. Available at Elsevier SSRN: https://ssrn.com/abstract=3168339 or http://dx.doi.org/10.2139/ssrn.3168339

      [2] ASN Chakravarthy, P S Avadhani, PESN Krishna Prasasd “A Novel Approach For Authenticating Textual Or Graphical Passwords Using Hopfield Neural Networkâ€, Advanced Computing: An International Journal ( ACIJ ), Vol.2, No.4, July 2011.

      [3] ASN Chakravarthy and Prof.P S Avadhani,†A Probabilistic Approach for Authenticating Text or Graphical Passwords Using Back Propagation,†IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.5, May 2011.

      [4] P. E. S. N. K. Prasasd, A. S. N. Chakravarthy and B. D. C. N. Prasad, “Performance evaluation of password authentication using associative neural memory models,†International Journal of Advanced Information Technology (IJAIT), vol. 2, no. 1, pp. 75–85, 2012.

      [5] Vachaspati, Pranjal, A. S. N. Chakravarthy, UCEV and Vizianagaram. “A Novel Soft Computing Authentication Scheme for Textual and Graphical Passwords.†(2013).

      [6] Specht D (1991) A general regression neural network. IEEE Trans Neural Networks 2(6):568–576.

      [7] Jacob Toft Pedersen, “Study group SURF: Feature detection & description†Published 2011, Q4 2011.

      [8] Herbert bay, T Tuytelaars, L Van Gool,“ Speed Up Robust Features (SURF)â€, Computer vision and Image Processing, Elsevier preprint,2008.

      [9] Matti Pietikäinen, Abdenour Hadid ,Guoying Zhao, Timo Ahonen, “ Local Binary Patterns for still images “, Computational Imaging and Vision book series (CIVI, volume 40), pp 13-47.

      [10] Ojala, T., Pietikäinen, M., Mäenpää, M.: “ Multiresolution gray-scale and rotation invariant texture classification with local binary patternsâ€. IEEE Trans. Pattern Anal.Mach. Intell. 24(7), 971–987 (2002)

      [11] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patternsâ€, Pattern Recognition, vol.42, issue.3, pp.425-436, 2009.

      [12] Awad, Ali & Hassaballah, M. (2016). Image Feature Detectors and Descriptors; Foundations and Applications. 10.1007/978-3-319-28854-3.

      [13] Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. IEEE Computer Vision and Pattern Recognition(CVPR).886-893.

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  • How to Cite

    Baby Maruthi, P., Scholar, R., & Tirupathi., S. (2018). General Regression Neural Network Approach for Image Transformation Based Hybrid Graphical Password Authentication System. International Journal of Engineering & Technology, 7(3.24), 454-460. https://doi.org/10.14419/ijet.v7i3.24.22792

    Received date: 2018-12-02

    Accepted date: 2018-12-02