Earticle

초록

영어

In this paper, we address the problem of unbalanced training dataset for source camera identification, namely, there are fewer training examples for some camera models compared to other camera models. A new source camera identification approach is proposed to alleviate the influence of imbalanced training dataset. In the proposed approach, firstly, we treat source camera identification as a multi-class classification problem, and decompose it into binary classification problems. After decomposing, the problem of imbalanced training dataset for multiclass classification is transformed to the problem of imbalanced training dataset for binary classification. Then, we incorporate SMOTE and AdaBoost algorithms to construct SVM ensemble to address the issue of imbalanced training dataset for binary classification. A number of experiments show the proposed approach can deal with the imbalanced training dataset effectively.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Image Acquisition Process Driven Source Camera Identification
  2.2. SVM Ensemble for Imbalanced Datasets
 3. A New Source Camera Identification Approach
  3.1. System Model
  3.2 The Proposed Approach
 4. Experiments and Results
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Yonggang Huang [ School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China ]
• Jun Zhang [ School of Information Technology, Deakin University, VIC, 3217, Australia ]
• Xinkai Lan [ Overhaul Branch, Beijing Electric Power Company, Beijing, China ]

참고문헌

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간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605