Forensic Camera Identification in Social Networks via Camera Fingerprint

Forensic Camera Identification in Social Networks via Camera Fingerprint

Tzuhuan Lin, Yu-Ru Wang
Copyright: © 2022 |Pages: 13
DOI: 10.4018/978-1-7998-8386-9.ch008
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Abstract

Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.
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Method

The proposed method that extracts the PRNU factor of the device, and evaluates the similarity between the PRNU factors of the disputed images and those of the device is proposed. Fig. 1 illustrates the flow chart of our proposed SCI method. The adaptive de-noising filter is first applied to images to extract the rough device artifacts. Then, the enhancement algorithm is applied to remove the non-unique artifacts. Next, to reduce the effect of rolling1, the obtained PRNU factors are rotated to the adaptive direction. For training images, the refined noise residues are averaged and then stored in the database. For disputed images, the modified signed peak correlation energy (MSPCE) statistics and a theoretical threshold are used to determine the source camera. The following subsections will describe each step of the proposed method in detail.

Figure 1.

The flow chart of our proposed method

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