mlsanityData to 7 Oct 2026

Discussion Paper: The Threat of Real Time Deepfakes

arXiv 2306.02487 ↗First posted 4 Jun 2023cs.AI cs.CR cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These ``deepfakes'' are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and today attackers have the ability to generate deepfakes in real-time. This new capability poses a significant threat to society as attackers begin to exploit the technology in advances social engineering attacks. In this paper, we discuss the implications of this emerging threat, identify the challenges with preventing these attacks and suggest a better direction for researching stronger defences.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 4 Jun 2023.

Builds on

8 of 21 references.

References · 8

  1. 2019Face X-ray for More General Face Forgery Detectioncited by 469
  2. 2020The Creation and Detection of Deepfakes: A Surveycited by 246
  3. 2020DeepFaceLab: Integrated, flexible and extensible face-swapping frameworkcited by 129
  4. 2020Evading Deepfake-Image Detectors with White- and Black-Box Attackscited by 80
  5. 2021StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversioncited by 57
  6. 2020DeepFake Detection: Current Challenges and Next Stepscited by 44
  7. 2020DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimationcited by 28
  8. 2023Deepfake CAPTCHA: A Method for Preventing Fake Callscited by 7

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.