mlsanityData to 7 Oct 2026

Recent Advancements In The Field Of Deepfake Detection

arXiv 2308.05563 ↗First posted 10 Aug 2023cs.AI cs.CYcited by 0

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Abstract (arXiv)

A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 10 Aug 2023.

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26 of 35 references.

References · 26

  1. 2018Exposing DeepFake Videos By Detecting Face Warping Artifactscited by 318
  2. 2020ID-Reveal: Identity-aware DeepFake Video Detectioncited by 97
  3. 2020DeepFake Detection by Analyzing Convolutional Tracescited by 81
  4. 2021Deepfake Video Detection Using Convolutional Vision Transformercited by 79
  5. 2021Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowdscited by 58
  6. 2021An Examination of Fairness of AI Models for Deepfake Detectioncited by 34
  7. 2022GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detectioncited by 32
  8. 2021Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentationcited by 25
  9. 2020Identity-Driven DeepFake Detectioncited by 17
  10. 2021Human Perception of Audio Deepfakescited by 15
  11. 2020Deepfake detection: humans vs. machinescited by 14
  12. 2022Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authenticationcited by 9
14 more
  1. 2022SpecRNet: Towards Faster and More Accessible Audio DeepFake Detectioncited by 9
  2. 2022Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learningcited by 8
  3. 2022A Close Look into Human Activity Recognition Models using Deep Learningcited by 8
  4. 2022DeePhy: On Deepfake Phylogenycited by 8
  5. 2022Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learningcited by 6
  6. 2022Using Deep Learning to Detecting Deepfakescited by 6
  7. 2022Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Datasetcited by 6
  8. 2022Aggregating Layers for Deepfake Detectioncited by 4
  9. 2022Exploring Spatial-Temporal Features for Deepfake Detection and Localizationcited by 4
  10. 2023Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVMcited by 3
  11. 2022Practical Deepfake Detection: Vulnerabilities in Global Contextscited by 2
  12. 2023Leveraging Deep Learning Approaches for Deepfake Detection: A Reviewcited by 2
  13. 2022Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detectioncited by 1
  14. 2023Hybrid Deepfake Detection Utilizing MLP and LSTMcited by 1

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