Deepfake detection using capsule networks and long short-term memory networks

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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 parameters, resulting in reduced computational cost.

Original languageEnglish
Title of host publicationProceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch
PublisherSCITEPRESS
Pages407-414
Number of pages8
ISBN (Electronic)9789897584886
DOIs
Publication statusPublished - 2021
Event16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021 - Virtual, Online
Duration: 8 Feb 202110 Feb 2021
Conference number: 16

Conference

Conference16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021
Abbreviated titleVISIGRAPP 2021
CityVirtual, Online
Period8/02/2110/02/21

Keywords

  • Capsule Networks
  • Deepfake Detection
  • Face Video Manipulation
  • Long Short-Term Memory Networks

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