Earticle

초록

영어

With advances in image display technology, recapturing good-quality images from high-fidelity artificial scenery on a LCD screen becomes possible. Forgers can recapture the artificially generated scenery and use the recaptured image to fool image forensic system. Image recapture detection is to distinguish real-scene images from the recaptured ones. An image recapture detection method based on multiple feature descriptors is proposed in this paper, which uses combinations of low-level features including texture, noise, difference histogram and color information. One hundred and thirty-six dimensions of features are extracted to train a support vector machine classifier with RBF kernel. Experimental results show that the proposed method is efficient with good detection rate of distinguishing real-scene images from the recaptured ones. It also possesses low dimensional features and low time complexity.

목차

Abstract
 1. Introduction
 2. Related Work
 3. The Proposed Image Recaptured Detection Method
  3.1. Low-Level Feature Extraction
  3.2. Classification
 4. Results and Discussion
  4.1. Feature Evaluation
  4.2. Datasets Evaluation
  4.3. Comparisons with Previous Works
 5. Conclusions
 References

키워드

저자

• Yongzhen Ke [ School of Computer Science and Software Engineering, Tianjin Polytechnic University ]
• Qingqing Shan [ School of Computer Science and Software Engineering, Tianjin Polytechnic University ]
• Fan Qin [ Department of Logistics Management, Nankai University ]
• Weidong Min [ School of Computer Science and Software Engineering, Tianjin Polytechnic University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605