A Jungle Computing approach to common image source identification in large collections of images

B. van Werkhoven, P. Hijma, C.J.H. Jacobs, J. Maassen, Z.J.M.H. Geradts, H.E. Bal
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mpetrfjxlffapitphr7jkntyou" style="color: black;">Digital Investigation. The International Journal of Digital Forensics and Incident Response</a> </i> &nbsp;
Analyzing digital images is an important investigation in forensics with the ever increasing number of images from computers and smartphones. In this article we aim to advance the state-of-the-art in common image source identification (which images originate from the same source camera). To this end, we present two types of applications for different goals that make use of a) a modern Desktop computer with a GPU and b) highly heterogeneous cluster computers with many different kinds of GPUs,
more &raquo; ... ething we call computing jungles. The first application targets medium-scale investigations, for example within a crime laboratory, the second application is targeted at large-scale investigations, for example within institutions. We advance the state-of-the-art by 1) explaining in detail how we obtain the performance to 2) support large databases of images in reasonable time while 3) not giving up accuracy. Moreover, we do not apply filtering ensuring that 4) our results are highly reproducible.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.diin.2018.09.002">doi:10.1016/j.diin.2018.09.002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cnx67nys4fc4zl3mrwykcqppna">fatcat:cnx67nys4fc4zl3mrwykcqppna</a> </span>
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