Open AccessPixel Value Graphical Password Scheme
Author(s) -
Mohd Afizi Mohd Shukran,
Muhammad Naim Abdullah,
Nur Adnin Ahmad Zaidi,
Norshahriah Abdul Wahab,
Mohd Fahmi Mohamad Amran,
Mohd Nazri Ismail,
Mohammad Adib Khairuddin,
Syed Muzzameer Syed Zulkiplee,
Faudziah Ahmad,
Mohd Rizal Mohd Isa,
Mohd Sidek Fadhil Mohd Yunus
Publication year - 2021
Publication title -
international journal of electrical and computer engineering systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.141
H-Index - 4
eISSN - 1847-7003
pISSN - 1847-6996
DOI - 10.32985/ijeces.12.si.3
Subject(s) - password , pixel , computer science , cluster analysis , computer vision , artificial intelligence , computer security
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 (RGB) 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 (CBIR) 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.