A new hybrid fuzzy biometric-based image authentication model

  • Authors:

  • Sherin M. Youssef;Yasser El-Sonbaty;Karma M. Fathalla

  • Affiliations:

  • Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt;College of Computing and IT, Arab Academy for Science and Technology, Alexandria, Egypt;Department of Computer Engineering, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt

  • Venue:
  • International Journal of Hybrid Intelligent Systems
  • Year:
  • 2012

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Abstract

Watermarking is one of the most known techniques for authentication, tampering detection, privacy control, etc. Concerning privacy protection of patients' medical records, efforts have been devoted to guarantee the confidentiality of data and medical images during storage and transmission via an untrustworthy channel. Our developed watermarking system, aims at fulfilling such demands through the utilization of a biometric authentication code sender physician's iris code, encrypted patient data and a fuzzy-based Region-of-Interest ROI segmentation algorithm. In this paper, a new hybrid DCT fuzzy biometric-based watermarking scheme has been introduced for privacy protection and source verification of medical and non-medical images. The proposed scheme integrates forward DCT transform with enhanced fuzzy-based ROI regions segmentation. To comply with the imperceptibility and high image quality requirements, the coefficients selection decision depends on perceptual visibility threshold estimation, based upon characteristics of the human visual system HVS. The inclusion of a just-noticeable distortion JND profile, computed for DCT coefficients, is proved to outperform the traditional DCT model, with the major contributions of a new formula for luminance adaptation adjustment and the incorporation of block classification for contrast masking. A block classification has been utilized to differentiate edge regions and thus effectively avoid over-estimation of JND in the selected regions. Moreover, several enhanced fuzzy-based clustering models have been suggested for extraction of ROI regions in medical patterns, which aim at increasing the robustness to noise and yield more homogeneous regions with less spurious blobs. The proposed hybrid fuzzy-based ROI extraction scheme integrates the effect of the local neighborhood and allow it to influence the membership value of each pixel. A new Hybrid FCM H-FCM algorithm is proposed, which integrates spatial information with a 2D adaptive noise removal SS-FCM model. Experiments have been conducted to verify the proposed model. Several attacks have been applied to the proposed scheme and the experiments revealed promising results in terms of visual quality and extracted watermark distortion.