Abstract
Nowadays, digital images are easily shared through social media and it common amongst internet users. It was a convenient way to share a moment and communicate with people all over the worlds through social media on the internet. However, this has caused the increasing number of crimes involving digital images in social media. It is well known that each digital image that passes through online social networks (OSNs) is explicitly modified by Web 2.0 tools. Thus, it is challenging for authorities to probe further, including identifying the source of the digital images. Considering this limitation, an alternative method to identify source camera based on the texture feature for OSNs images is proposed. This technique uses texture feature characteristics, namely, Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run-Length Matrix (GLRLM). Original and OSNs images were tested to determine whether the proposed method is robust for both image types and gives higher accuracy than previous methods. Four types of camera models were used in this research. The results prove that the method tested in this study is accurate with an average accuracy of 97.00% and 99.59% for original and OSNs images, respectively, and is capable to read up to 600 images.
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Rahim, N., Foozy, C.F.M. (2020). Source Camera Identification for Online Social Network Images Using Texture Feature. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_28
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