mlsanityData to 7 Oct 2026

Challenges and Solutions in DeepFakes

arXiv 2109.05397 ↗First posted 12 Sep 2021cs.CVcited by 0

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Abstract (arXiv)

Deep learning has been successfully appertained to solve various complex problems in the area of big data analytics to computer vision. A deep learning-powered application recently emerged is Deep Fake. It helps to create fake images and videos that human cannot distinguish them from the real ones and are recent off-shelf manipulation technique that allows swapping two identities in a single video. Technology is a controversial technology with many wide-reaching issues impacting society. So, to counter this emerging problem, we introduce a dataset of 140k real and fake faces which contain 70k real faces from the Flickr dataset collected by Nvidia, as well as 70k fake faces sampled from 1 million fake faces generated by style GAN. We will train our model in the dataset so that our model can identify real or fake faces.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 58 references that are in the map. First posted on arXiv 12 Sep 2021.

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30 of 58 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2014Going Deeper with Convolutionscited by 4,231
  6. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  7. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  9. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
  10. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  11. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
  12. 2019Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensicscited by 573
18 more
  1. 2019Face X-ray for More General Face Forgery Detectioncited by 469
  2. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 318
  3. 2020StyleRig: Rigging StyleGAN for 3D Control over Portrait Imagescited by 267
  4. 2020DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detectioncited by 231
  5. 2020Global Texture Enhancement for Fake Face Detection in the Wildcited by 220
  6. 2018DeepFakes: a New Threat to Face Recognition? Assessment and Detectioncited by 208
  7. 2019The Deepfake Detection Challenge (DFDC) Preview Datasetcited by 208
  8. 2018Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videoscited by 180
  9. 2020The DeepFake Detection Challenge (DFDC) Datasetcited by 125
  10. 2019Detecting GAN generated Fake Images using Co-occurrence Matricescited by 124
  11. 2016Fast Face-swap Using Convolutional Neural Networkscited by 119
  12. 2019Recurrent Convolutional Strategies for Face Manipulation Detection in Videoscited by 118
  13. 2019MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targetscited by 90
  14. 2020DeepFake Detection by Analyzing Convolutional Tracescited by 81
  15. 2018Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformationcited by 49
  16. 2020Deepfake Detection using Spatiotemporal Convolutional Networkscited by 32
  17. 2019Transformable Bottleneck Networkscited by 23
  18. 2018Recent Advances in Convolutional Neural Network Accelerationcited by 17

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