Autoencoder with recurrent neural networks for video forgery detection

Dario D'Avino, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva
<span title="2017-01-29">2017</span> <i title="Society for Imaging Science &amp; Technology"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sajjfb6z3be2xbfjs4d2afwzyi" style="color: black;">IS&amp;T International Symposium on Electronic Imaging Science and Technology</a> </i> &nbsp;
Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the
more &raquo; ... d model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2352/issn.2470-1173.2017.7.mwsf-330">doi:10.2352/issn.2470-1173.2017.7.mwsf-330</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jtoxss4ej5hrtmxodi2eihjqcu">fatcat:jtoxss4ej5hrtmxodi2eihjqcu</a> </span>
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