NDLI: K-unknown models detection through clustering in blind source camera identification

Content Provider	IET Digital Library
Author	Sameer, Venkata Udaya S, Sugumaran Naskar, Ruchira
Abstract	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.
Starting Page	1204
Ending Page	1213
Page Count	10
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 7, Jul (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1142
Journal	IET Image Processing
Publisher Date	2018-02-23
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blind Source Camera Identification Blind Source Separation Camera Clustering Computer Vision And Image Processing Technique Cybercrime Image Forensics Image Mapping Forensic Problem K-unknown Model Detection Optical, Image And Video Signal Processing Pattern Clustering SCI Scheme
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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