Research Article Open Access

Design an Advance Computer-Aided Tool for Image Authentication and Classification

Rozita Teymourzadeh1, Amirize Alpha Laadi1, Yazan Samir Algnabi2, M.D. Shabul Islam2, Sawal H.M. Ali3 and Masuri Othman2
  • 1 Department of Electrical and Electronic, Faculty of Engineering, Technology and Built Environment UCSI University, Jalan Choo Lip Kung, Taman Taynton View, 56000 Cheras, Kuala Lumpur, Malaysia
  • 2 Department of VLSI Design, Institute of Micro Engineering and Nanoelectronics IMEN, Malaysia
  • 3 Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia


Over the years, advancements in the fields of digital image processing and artificial intelligence have been applied in solving many real-life problems. This could be seen in facial image recognition for security systems, identity registrations. Hence a bottleneck of identity registration is image processing. These are carried out in form of image preprocessing, image region extraction by cropping, feature extraction using Principal Component Analysis (PCA) and image compression using Discrete Cosine Transform (DCT). Other processing include filtering and histogram equalization using contrast stretching is performed while enhancing the image as part of the analytical tool. Hence, this research work presents a universal integration image forgery detection analysis tool with image facial recognition using Black Propagation Neural Network (BPNN) processor. The proposed designed tool is a multi-function smart tool with the novel architecture of programmable error goal and light intensity. Furthermore, its advance dual database increases the efficiency for high performance application. With the fact that, the facial image recognition will always, give a matching output or closest possible output image for every input image irrespective of the authenticity, the universal smart GUI tool is proposed and designed to perform image forgery detection with the high accuracy of ±2% error rate. Meanwhile, a novel structure that provides efficient automatic image forgery detection for all input test images for the BPNN recognition is presented. Hence, an input image will be authenticated before being fed into the recognition tool.

American Journal of Applied Sciences
Volume 10 No. 7, 2013, 696-705


Submitted On: 11 May 2012 Published On: 24 June 2013

How to Cite: Teymourzadeh, R., Laadi, A. A., Algnabi, Y. S., Islam, M. S., Ali, S. H. & Othman, M. (2013). Design an Advance Computer-Aided Tool for Image Authentication and Classification. American Journal of Applied Sciences, 10(7), 696-705.

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  • Principal Component Analysis (PCA)
  • Discrete Cosine Transform (DCT)
  • Black Propagation Neural Network (BPNN)
  • Local Binary Pattern (LBP)