On the Performance of Li’s Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations

On the Performance of Li’s Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations

Ahmad Ryad Soobhany (Keele University and Forensic Pathways Ltd., UK), Richard Leary (Forensic Pathways Ltd., UK) and KP Lam (Keele University, UK)
Copyright: © 2011 |Pages: 13
DOI: 10.4018/jdcf.2011010101

Abstract

Images from digital imaging devices are prevalent in society. The signatures of these images can be extracted as sensor pattern noise (SPN) and classified according to their source devices. In this paper, the authors assess the reliability of an unsupervised classifier for forensic investigation of digital images recovered from storage devices and to identify the best position for cropping the images before processing. Cross validation was performed on the classifier to assess the error rate and determine the effect of the size of the sample space and the classifier trainer on the performance of the classifier. Moreover, the authors find that the effect of saturation and subsequently the contamination of the SPN in the images affected performance negatively. To alleviate the negative performance, the authors identify the areas of images where less contamination occurs to perform cropping.
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Image Acquisition Process

In the field of digital image forensics (Figure 1), research has been undertaken to help identify the source of digital images by extracting and using their digital signatures. The various image-processing stages or components used in a digital camera leave traces in the resultant images produced and these artefacts can be used to identify the source device. Figure 1 depicts these different stages in the image acquisition process in the camera pipeline.

Figure 1.

Image acquisition process inside a digital camera

The light enters the camera through the lens, where the light is focused and the light passes through the anti-aliasing filter. This filter acts as a low-pass filter to prevent spatial frequencies higher than that of the individual pixel in the sensor from passing through, which will otherwise create aliasing (Moiré effect). The colour filter array (CFA) is a filter that will capture the colour components of the light stream. There are different types of CFAs and the Bayer filter, shown in the figure, stores the red, green and blue (RGB) colours where each pixel will store one colour and interpolates the other two colours from the neighbouring pixels. Camera manufacturers use different methods, known as CFA interpolation and demosaicing, to calculate the remaining colours that the pixel does not store. The sensor is the most expensive component of the camera and is usually of two main types, the complementary metal–oxide–semiconductor (CMOS) and the charge-coupled device (CCD). Traditionally, CCD has been more commonly used but CMOS is being used more often, mainly in mobile (cell) phones cameras, because it consumes less power.

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