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Authors: Akul Mehra ; Luuk Spreeuwers and Nicola Strisciuglio

Affiliation: Data Management and Biometrics Group, University of Twente, Enschede, The Netherlands

Keyword(s): Deepfake Detection, Face Video Manipulation, Capsule Networks, Long Short-Term Memory Networks.

Abstract: With the recent advancements of technology, and in particular with graphics processing and artificial intelligence algorithms, fake media generation has become easier. Using deep learning techniques like Deepfakes and FaceSwap, anyone can generate fake videos by manipulating the face/voice of target subjects in videos. These AI synthesized videos are a big threat to the authenticity and trustworthiness of online information and can be used for malicious purposes. Detecting face tampering in videos is of utmost importance. We propose a spatio-temporal hybrid model of Capsule Networks integrated with Long Short-Term Memory (LSTM) networks. This model exploits the inconsistencies in videos to distinguish real and fake videos. We use three different frame selection techniques and show that frame selection has a significant impact on the performance of models. The combined Capsule and LSTM network have comparable performance to state-of-the-art models and about 1/5th the number of paramet ers, resulting in reduced computational cost. (More)

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Paper citation in several formats:
Mehra, A.; Spreeuwers, L. and Strisciuglio, N. (2021). Deepfake Detection using Capsule Networks and Long Short-Term Memory Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 407-414. DOI: 10.5220/0010289004070414

@conference{visapp21,
author={Akul Mehra. and Luuk Spreeuwers. and Nicola Strisciuglio.},
title={Deepfake Detection using Capsule Networks and Long Short-Term Memory Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={407-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010289004070414},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Deepfake Detection using Capsule Networks and Long Short-Term Memory Networks
SN - 978-989-758-488-6
IS - 2184-4321
AU - Mehra, A.
AU - Spreeuwers, L.
AU - Strisciuglio, N.
PY - 2021
SP - 407
EP - 414
DO - 10.5220/0010289004070414
PB - SciTePress

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