KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Printer Identification Methods Using Global and Local Feature-Based Deep Learning

전역 및 지역 특징 기반 딥러닝을 이용한 프린터 장치 판별 기술

  • 이수현 (금오공과대학교 소프트웨어공학과) ;
  • 이해연 (금오공과대학교 컴퓨터소프트웨어공학과)
  • Received : 2018.10.04
  • Accepted : 2018.12.01
  • Published : 2019.01.31
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Abstract

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

디지털 IT 기술의 발달로 인하여 프린터와 스캐너의 성능이 향상되고 가격이 저렴해지면서 일반인들도 쉽게 접할 수 있게 되었다. 그러나 이에 따른 부작용으로 공문서 및 사문서 위조 등의 범죄들이 쉽게 이루어질 수 있다. 따라서 해당 문서가 어떤 프린터를 사용하여 출력 되었는가를 특정할 수 있다면 수사 범위를 줄이고 용의자를 판별하는데 도움이 된다. 본 논문에서는 프린터 장치 판별을 위하여 딥러닝 모델을 제안한다. 먼저 최근 인식 등에서 범용적으로 활용되는 지역 특징 기반의 컨볼루셔널 뉴널 네트워크를 이용한 프린터 장치 판별 모델을 제안하고, 전역 특징 기반의 처리 과정을 네트워크 모델에 도입함으로 인하여 수렴 속도 및 정확도를 향상한 기법을 제안한다. 제안한 모델의 성능은 8개의 프린터 장치를 활용하여 기존 프린터 판별을 위한 특징 기반 기술과 비교를 수행하였다. 그 결과 제안하는 지역 특징 기반의 모델과 전역 특징 기반의 모델이 각각 97.23% 및 99.98%의 높은 판별 정확도를 달성하였고, 기존 기술들에 비하여 높은 정확도를 갖는 우수성을 보였다.

Keywords

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Fig. 1. Printer Identification using Wiener Filter and GLCM

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Fig. 2. General Structure of CNN

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Fig. 3. Training and Testing Process of Proposed Algorithm

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Fig. 4. Local Feature-based Printer Identification Model

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Fig. 5. Gray Level Co-occurrence Matrix Calculation(in case of 3 bits image)

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Fig. 6. GLCM Feature Map Processing for Data Reduction

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Fig. 7. Samples from Each Printer

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Fig. 8. 128x128 Sample Collection with Random Cropping

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Fig. 9. Printer Identification Accuracy of Two Proposed Models

Table 1. List of Printer Used for Experiment and Their Labels

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Table. 2 Printer Identification Accuracy Per Epoch

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Table 3. Comparison of Printer Identification Algorithms

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Table 4. Printer identification Accuracy for Each Printer Using Local Feature Model

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Table 5. Printer Identification Accuracy for Each Printer Using Global Feature Model

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