Effective Source Camera Identification based on MSEPLL Denoising Applied to Small Image Patches

Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes; however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a sample camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.

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Source camera identification using Photo Response Non-Uniformity on WhatsApp

Digital Investigation ◽

10.1016/j.diin.2018.02.005 ◽

2018 ◽

Vol 24 ◽

pp. 142-154 ◽

Cited By ~ 2

Author(s):

Christiaan Meij ◽

Zeno Geradts

Keyword(s):

Source Camera Identification ◽

Camera Identification ◽

Photo Response

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Source Camera Identification Based on Sensor Readout Noise

International Journal of Digital Crime and Forensics ◽

10.4018/jdcf.2010070103 ◽

2010 ◽

Vol 2 (3) ◽

pp. 28-42 ◽

Cited By ~ 1

Author(s):

H. R. Chennamma ◽

Lalitha Rangarajan

Keyword(s):

Computer Software ◽

Sensor Data ◽

Processing Parameter ◽

Source Camera Identification ◽

Camera Identification ◽

Novel Approach ◽

Deviation Plot ◽

Readout Noise ◽

The One ◽

Imaging Sensor

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.

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Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification

International Journal of Digital Crime and Forensics ◽

10.4018/jdcf.2010040102 ◽

2010 ◽

Vol 2 (2) ◽

pp. 21-33 ◽

Cited By ~ 7

Author(s):

Irene Amerini ◽

Roberto Caldelli ◽

Vito Cappellini ◽

Francesco Picchioni ◽

Alessandro Piva

Keyword(s):

Noise Model ◽

Digital Content ◽

Wavelet Domain ◽

Multimedia Forensics ◽

Source Camera Identification ◽

Camera Identification ◽

Novel Method ◽

Photo Response ◽

Noise Models ◽

Open Issues

Identification of the source that has generated a digital content is considered one of the main open issues in multimedia forensics community. The extraction of photo-response non-uniformity (PRNU) noise has been so far indicated as a mean to identify sensor fingerprint. Such a fingerprint can be estimated from multiple images taken by the same camera by means of a de-noising filtering operation. In this paper, the authors propose a novel method for estimating the PRNU noise in source camera identification. In particular, a MMSE digital filter in the un-decimated wavelet domain, based on a signal-dependent noise model, is introduced and compared with others commonly adopted for this purpose. A theoretical framework and experimental results are provided and discussed.

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