Pixel Value Graphical Password Scheme: K-Means as Graphical Password Fault Tolerance

被引:0
作者
Shukran, Mohd Afizi Mohd [1 ]
Khairuddin, Mohammad Adib [1 ]
Yunus, Mohd Sidek Fadhil Mohd [2 ]
Ismail, Mohd Nazri [1 ]
Isa, Mohd Rizal Mohd [1 ]
Amran, Mohd Fahmi Mohamad [1 ]
Ahmad, Fatimah [1 ]
Wahab, Norshahriah [1 ]
Abdullah, Muhammad Naim [3 ]
Zaidi, Nur Adnin Ahmad [1 ]
Zuikiplee, Syed Muzzameer Syed [1 ]
机构
[1] Natl Def Univ Malaysia, Fac Def Sci & Technol, Dept Comp Sci, Kuala Lumpur, Malaysia
[2] Sultan Idris Educ Univ, Fac Arts Comp & Ind Creat, Dept Comp, Tanjung Malim, Malaysia
[3] Univ Malaysia Comp Sci & Engn UNIMY, Dept Comp, Acad Affairs, Serdang, Selangor, Malaysia
关键词
Cybersecurity; PVAC; Pixel Value; Graphical Password; Clustering; K-Means;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Pixel value access control (PVAC) was introduced to deliver a secure and simple graphical password method where it requires users to load their image as their password. PVAC extracts the image to obtain a three-octet 8-bits Red-Green-Blue (RG8) value as its password to authenticate a user. The pixel value must be matched with the record stored in the database or otherwise, the user is failed to authenticate. However, users which prefer to store images on cloud storage would unintentionally alter and as well as the pixel value due to media compression and caused faulty pixels. Thus, the K-Means clustering algorithm is adapted to fix the issue where the faulty pixel value would be recognized as having the same pixel value cluster as the original. However, most of K-Means algorithm works were mainly developed for content-based image retrieval (C8IR) which having opposite characteristics from PVAC. Thus, this study was aimed to investigate the crucial criteria of PVAC and its compatibility with the K-Means algorithm for the problem. The theoretical analysis is used for this study where the suitable characteristics of K-Means are analyze based on PVAC requirements. The compliance analysis might become a referencing work for digital image clustering techniques adaptation on security system such as image filtering, image recognition, and object detection since most of image clustering works was focused on less sensitive image retrieval.
引用
收藏
页码:23 / 29
页数:7
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