Korshunov, PavelHalstead, MichaelCastan, DiegoGraciarena, MartinMcLaren, MitchellBurns, BrianLawson, AaronMarcel, Sébastien2019-09-052019-09-052019-09-052019https://infoscience.epfl.ch/handle/20.500.14299/160882The recent increase in social media based propaganda, i.e., ‘fake news’, calls for automated methods to detect tampered content. In this paper, we focus on detecting tampering in a video with a person speaking to a camera. This form of manipulation is easy to perform, since one can just replace a part of the audio, dramatically chang- ing the meaning of the video. We consider several detection approaches based on phonetic features and recurrent networks. We demonstrate that by replacing standard MFCC features with embeddings from a DNN trained for automatic speech recognition, combined with mouth landmarks (visual features), we can achieve a significant performance improvement on several challenging publicly available databases of speakers (VidTIMIT, AMI, and GRID), for which we generated sets of tampered data. The evaluations demonstrate a relative equal error rate reduction of 55% (to 4.5% from 10.0%) on the large GRID corpus based dataset and a satisfying generalization of the model on other datasets.inconsistencies detectionlip-syncingVideo tamperingtext::conference output::conference proceedings::conference paper