Earticle

초록

영어

In this paper, we propose a camera-model detection method based on a hybrid approach. Varying camera inside imaging processing will lead to varying artifacts. A few of artifacts can reflect camera model-specific. To comprehensive track camera model-specific footprint, we build a hybrid approach by combining two-step Markov feature based model and CFA feature based model. A 132-D feature set is designed to perform camera-model classification. Images from seven camera models in the Dresden Image Database are chosen as our experiment database. Experiment results show that in seven models detection, the average detection accuracy of our method is 99.83%. Even the feature dimension is decreased to 40 by feature selection; its detection accuracy can still reach to 99.58%, which is higher than that of previous Markov method [12].

목차

Abstract
 1. Introduction
 2. Hybrid Forensics Model for Detection
 3. Markov Feature
  3.1. Difference JPEG 2-D Array
  3.2. Two-step Transition Probability Matrix with Threshold Setting
 4. CFA Feature
 5. Experiment
 6. Conclusion
 References

키워드

저자

• Shang Gao [ Information Center of Guangdong Power Grid Co., Ltd. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605