王波

Professor   Supervisor of Doctorate Candidates   Supervisor of Master's Candidates

Main positions:Professor

Gender:Male

Alma Mater:Dalian University of Technology

Degree:Doctoral Degree

School/Department:School of Information and Communication Engineering

Discipline:Signal and Information Processing

Business Address:A512, Haishan Building

Contact Information:bowang@dlut.edu.cn

E-Mail:bowang@dlut.edu.cn


Paper Publications

PATCH-BASED SENSOR PATTERN NOISE FOR CAMERA SOURCE IDENTIFICATION

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Indexed by:会议论文

Date of Publication:2015-07-12

Included Journals:EI、CPCI-S、Scopus

Page Number:866-870

Key Words:Sensor pattern noise; source camera identification; image complexity; patch-based sensor pattern noise

Abstract:Sensor pattern noise (SPN) has been proved to be an inherent fingerprint of a camera, and it has been broadly used in the fields of image authentication and camera source identification. However, the SPN extracted using current denoising algorithm always contains image content residual, which would significatively influence the accuracy of camera source identification. In this paper, a novel patch-based (PB) sensor pattern noise algorithm for camera source identification is proposed to solve this problem. Low-complexity patches of images are selected to construct local reference SPN, which contains least image content residual. The global reference SPN is constituted with the block-wised local SPN. Similarly for the test image, SPN is extracted from low-complexity region, and making correlation with corresponding local reference SPN. Our experiments on the Dresden database demonstrate that the proposed approach outperforms two sensor pattern noise estimation methods on the literatures as baseline.

Pre One:SECURITY ANALYSIS OF OPTIMAL MULTI-CARRIER SPREAD-SPECTRUM EMBEDDING

Next One:有限样本条件下的相机来源鉴别方法

Profile

I am not a star professor, but I am working on the road to a star professor of tomorrow. For this reason, self-motivated graduate students are desired to my research group. Self-motivated attitude and initiative are the most important characteristics in our laboratory. Students with preliminary knowledge on signal (image/video) processing and programming skills are most welcome. For your academic trip in my group, I will devote myself to training your FIVE abilities: Intellectual skills, Communication skills, Personality characteristics, Habit of work, Mechanical skills, which are considered as the most important abilities of a graduated student.

http://ice.dlut.edu.cn/WangBo/index.html