Abstract
The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.
Highlights
In recent years, different social networks (SNs) have revolutionized the web by providing users with specific types of interaction, for instance by sending texts and sharing images and videos
An important outcome of our work is presenting the inter-layer user profile linking (UPL) task, which is more desirable in digital investigations as it can link user profiles on different SNs
Let True Positive (TP) be a set of images to which the method has correctly assigned class labels, while that it has correctly not assigned is represented by True Negative (TN)
Summary
Different social networks (SNs) have revolutionized the web by providing users with specific types of interaction, for instance by sending texts and sharing images and videos. Once a digital crime is reported on an SN platform, the police may identify a number of suspects (e.g., friends, relatives and most active users) and collect the electronic devices and the respective profile information on the SNs. With a set of “original images” coming directly from a specific number of the collected devices and the “shared images” taken from suspects’ profiles, smartphone identification (SI) and user profile linking (UPL) could be achieved. With a set of “original images” coming directly from a specific number of the collected devices and the “shared images” taken from suspects’ profiles, smartphone identification (SI) and user profile linking (UPL) could be achieved These tasks represent an orthogonal work compared with the work presented in [5] and can provide the police with significant findings and the opportunity to update their dataset to apply to future investigations by creating new fingerprints of the criminals’ smartphones
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
