IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Source Camera Identification Based on Sensor Readout Noise

Source Camera Identification Based on Sensor Readout Noise
View Sample PDF
Author(s): H. R. Chennamma (University of Mysore, India)and Lalitha Rangarajan (University of Mysore, India)
Copyright: 2010
Volume: 2
Issue: 3
Pages: 15
Source title: International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/jdcf.2010070103

Purchase

View Source Camera Identification Based on Sensor Readout Noise on the publisher's website for pricing and purchasing information.

Abstract

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

Related Content

Shakir A. Mehdiyev, Tahmasib Kh. Fataliyev. © 2024. 17 pages.
Fuhai Jia, Yanru Jia, Jing Li, Zhenghui Liu. © 2024. 13 pages.
Dawei Zhang. © 2024. 16 pages.
Yuwen Zhu, Lei Yu. © 2023. 16 pages.
Vijay Kumar, Sahil Sharma, Chandan Kumar, Aditya Kumar Sahu. © 2023. 14 pages.
Wenjun Yao, Ying Jiang, Yang Yang. © 2023. 20 pages.
Dawei Zhang. © 2023. 14 pages.
Body Bottom