Similar DSLR Processor Identification Using Compact Model Templates
Fast proliferation of digital images have aroused forensics needs to identify and verify the source class for a given image patch. Statistical forensics features are known to be useful to fingerprint traditional source classes, such as device models and brands. The key challenge is to construct compact templates based on the features that are discriminant for different classes and stable for devices of the same class. While dissimilar camera sources are relatively easy to discriminate, this paper proposes to identify very similar digital single-lens reflex (DSLR) cameras on a new source class, i.e. the digital processor used. By computing several types of derivative correlation features, an eigenfeature extraction technique is employed to learn the compact model templates. The source processor is identified through finding the nearest neighbor model template. Our compact templates achieve an average accuracy of 96.0％ in identifying five DSLR processors from Canon brand using small image patches.
Hong Cao Alex C. Kot
Nanyang Technological University, Singapore Institute for Infocomm Research, A*STAR, Singapore Nanyang Technological University, Singapore
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-5
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）