Tampered Speaker Inconsistency Detection with Phonetically Aware Audio-visual Features
| Type of publication: | Conference paper |
| Citation: | Korshunov_AVFAKES_ICML_2019 |
| Publication status: | Published |
| Booktitle: | International Conference on Machine Learning |
| Series: | Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes |
| Year: | 2019 |
| Month: | July |
| Note: | Best paper award in ICML workshop "Synthetic Realities: Deep Learning for Detecting AudioVisual Fakes" |
| Abstract: | The 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. |
| Keywords: | inconsistencies detection, lip-syncing, Video tampering |
| Projects: |
Idiap SAVI |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
