Autoencoder with recurrent neural networks for video forgery detection

TL;DR

This paper proposes a deep learning approach using autoencoders and recurrent neural networks to detect video forgeries by identifying anomalies that do not conform to the learned source model.

Contribution

It introduces a novel architecture combining autoencoders with LSTM-based recurrent networks for effective video forgery detection.

Findings

Preliminary results show promising detection capabilities.

The method exploits temporal dependencies for improved accuracy.

Autoencoders learn an intrinsic source model from pristine frames.

Abstract

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 learned 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.