Autoencoder with recurrent neural networks for video forgery detection

@article{DAvino2017AutoencoderWR,
  title={Autoencoder with recurrent neural networks for video forgery detection},
  author={Dario D'Avino and Davide Cozzolino and Giovanni Poggi and Luisa Verdoliva},
  journal={ArXiv},
  year={2017},
  volume={abs/1708.08754}
}
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. [] Key Method 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 learned model, and is encoded with a large reconstruction error.
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