Open AccessK ‐unknown models detection through clustering in blind source camera identification
Author(s) -
Udaya Sameer Venkata,
S Sugumaran,
Naskar Ruchira
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1142
Subject(s) - identification (biology) , computer science , artificial intelligence , set (abstract data type) , computer vision , relation (database) , scheme (mathematics) , cluster analysis , image (mathematics) , pattern recognition (psychology) , data mining , mathematics , botany , biology , mathematical analysis , programming language
Source camera identification (SCI) is a forensic problem of mapping an image back to its source, often in relation to cybercrime. In this digital era, this problem needs to be addressed with the utmost care as a falsely identified source might implicate an innocent person. A very practical problem in this study is the presence of unknown models in the set of cameras under question. In other words, the images under question might not have originated from any of the camera models that are accessible to the forensic analyst, but from a different inaccessible source. Under such a circumstance, the conventional source detection techniques fail to identify the correct source, and falsely map the image to one of the accessible camera models. To address this problem, here the authors propose an ( N + K ) SCI scheme which is capable of identifying N known (accessible) as well as K unknown (inaccessible) camera models. The authors’ experimental results prove that the proposed scheme efficiently separates the known and unknown models, and helps to achieve considerably high source identification accuracy as compared to the state‐of‐the‐art.