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Deepfake Detection and the Impact of Limited Computing Capabilities

arXiv 2402.14825 ↗First posted 8 Feb 2024cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique. To ensure the accuracy of information and control misinformation and mass manipulation, it is of paramount importance to discover and develop artificial intelligence models that enable the generic detection of forged videos. This work aims to address the detection of deepfakes across various existing datasets in a scenario with limited computing resources. The goal is to analyze the applicability of different deep learning techniques under these restrictions and explore possible approaches to enhance their efficiency.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 8 Feb 2024.

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References · 19

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7 more
  1. 2020Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlightscited by 59
  2. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  3. 2023Toward Understanding Why Adam Converges Faster Than SGD for Transformerscited by 50
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  6. 2020Fake Generated Painting Detection via Frequency Analysiscited by 6
  7. 2022The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wildcited by 6

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