Earticle

초록

영어

Due to the significant advances of digital photography and the availability of many powerful photo editing tools, it becomes easier to create forgery images by non-professional users. Median filtering that is usually applied to erase the forensically significant fingerprints has recently received increased attention. In this paper, we present an effective blind forensic algorithm to detect the median filtering manipulation. First, the median filtered residual (MFR) is generated by computing the difference between a testing image and a median filtered version of itself. Then, three feature sets including histogram, autocorrelation and gradient are extracted from the median filter residual. Last, those features are fetched into support vector machine (SVM) for training and classification. Our experimental results demonstrate that our proposed forensic method achieves not only better detection rate but also lower computational complexity compared with other existing median filtering detection methods. Our proposed forensic method also can locate local median filtering of image effectively.

목차

Abstract
 1. Introduction
 2. Related Work
 3. The Proposed Algorithm
  3.1. Analysis of Median Filtering
  3.2. Feature Sets for Median Filter Detection
  3.3. A Blind Detection Algorithm of Median Filtered Image
 4. Results and Discussion
  4.1. The Classification Performance
  4.2. Detecting Local Median Filtering in an Image
 5. Conclusions
 Acknowledgements
 References

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605