Hybrid Clustering of Shared Images on Social Networks for Digital Forensics

Abstract

Clustering the images shared through social network (SN) platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The sensor pattern noise (SPN) caused by the camera sensor imperfections during the manufacturing process can be extracted from the images and used to fingerprint the smartphones. The process of content compression performed by the SNs causes loss of image details and weakens the SPN, making the clustering task even more challenging. In this paper, we present a hybrid algorithm capable of clustering the images captured and shared through SNs without prior knowledge about the types and number of the acquisition smartphones. The hybrid method exploits batch partitioning, image resizing, hierarchical and graph-based clustering approaches to cluster the images. Using Markov clustering, the hierarchical clustering is conducted in such a way that the representative clusters with a higher probability of belonging to the same camera are selected for merging, which accelerates the clustering. For merging the clusters, the adaptive threshold updated iteratively through the hybrid clustering is used, which results in more precise clusters even for images from the same model of smartphones. The results on the VISION dataset, including both native and shared images, prove the effectiveness and efficiency of the hybrid method in comparison with the state-of-the-art SPN-based image clustering algorithms.INDEX TERMS Sensor pattern noise, camera fingerprinting, image clustering, social networks, digital forensics.

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

“…Hence, we considered a scenario in which the number of smartphones has to be provided. Although in some applications it is not and clustering is used instead [ 5 , 23 ], applying classification is preferable which provides more accurate results compared with clustering.…”

“…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.…”

Media forensics on social media platforms: a survey

“…The first work in this sense involving images computes image similarity based on noise residuals [5] through a consensus clustering [19]. The work in [20] presents an algorithm to cluster images shared through SNs without prior knowledge about the types and number of the acquisition smartphones, as well as in [19] (zero knowledge approaches), with the difference that more than one SN have been considered in this case. This method exploits batch partitioning, image resizing, hierarchical and graph-based clustering to group the images which results in more precise clusters for images taken with the same smartphone model.…”

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

“…Recently, the PRNU-based method has been used to perform blind clustering on both pristine images and compressed images downloaded from SNs, addressing the source camera identification task [ 45 , 46 ]. In [ 47 ], the authors investigated the possibility of privacy leaks related to Photo-Response Non-Uniformity noise.…”