Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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An Enhancement Method of Document Restoration Capability using Encryption and DnCNN

암호화와 DnCNN을 활용한 문서 복원능력 향상에 관한 연구

  • Jang, Hyun-Hee (Korea Polytechnic College, New Technology Education Institute) ;
  • Ha, Sung-Jae (Korea Polytechnic College, Artificial Intelligence Department) ;
  • Cho, Gi-Hwan (Jeonbuk National University, Division of Computer Science and Engineering)
  • 장현희 (한국폴리텍대학 신기술교육원) ;
  • 하성재 (한국폴리텍대학 AI융합과) ;
  • 조기환 (전북대학교 컴퓨터공학부)
  • Received : 2022.02.08
  • Accepted : 2022.03.21
  • Published : 2022.04.30
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Abstract

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

본 논문은 문서의 보안과 손실 및 오염에 대하여 복원능력을 향상시키는 방안을 제안한다. 이를 위해서 암호화로 DnCNN(DeNoise Convolution Neural Network)을 제시한다. 암호화 방법을 구현하기 위하여 2D이미지정보를 광학에 사용되는 공간주파수 전달함수(Spatial Frequency Transfer Function)의 수학적 모델을 적용한다. 공간 주파수 전달함수를 사용하여 광학적 간섭 패턴을 암호화로 사용하고 공간 주파수 전달함수의 수학적 변수를 복호화하는 암호로 사용하는 방법을 제안하였다. 또한, 딥러닝을 적용한 DnCNN 방법을 적용하여 노이즈 제거하여 복원 성능을 개선한다. 실험결과, 65%의 정보 손실이 있는 경우에도 Pre-Training DnCNN Deep Learning을 적용한 결과 공간 주파수 전달함수만을 활용한 복원 결과 와 비교하여 PSNR(Peak Signal-to-noise ratio)을 11% 이상 우수한 성능을 확인할 수 있다. 또한, CC(Correlation Coefficient)의 특성도 16% 이상 우수한 결과를 보이고 있다.

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