Blind Image Source Device Identification: Practicality and Challenges

Blind Image Source Device Identification: Practicality and Challenges

Udaya Sameer Venkata (Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India) and Ruchira Naskar (Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India)
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJISP.2018070105

Abstract

This article describes how digital forensic techniques for source investigation and identification enable forensic analysts to map an image under question to its source device, in a completely blind way, with no a-priori information about the storage and processing. Such techniques operate based on blind image fingerprinting or machine learning based modelling using appropriate image features. Although researchers till date have succeeded to achieve extremely high accuracy, more than 99% with 10-12 candidate cameras, as far as source device prediction is concerned, the practical application of the existing techniques is still doubtful. This is due to the existence of some critical open challenges in this domain, such as exact device linking, open-set challenge, classifier overfitting and counter forensics. In this article, the authors identify those open challenges, with an insight into possible solution strategies.
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Source camera identification has been solved following two primary approaches. First, using camera fingerprints (Lukas, 2006), and second, through machine learning based model (Kharrazi et al., 2004). In the camera fingerprinting based techniques, Photo Response Non-Uniformity (PRNU) (Lukas, 2006) noise, a unique fingerprint formed on the camera's sensor while an image is captured, acts as the primary attribute to map an image to its source. Every camera manufacturer uses different sensors for different devices. The photo--electronic conversion of incident light to digital form, generates a noise at each pixel location of the sensor, hence producing a noise pattern, completely unique to the underlying sensor and thus the camera device.

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