Cited by 4Digital Media Forensic DetectionMisinformation and Its ImpactsBiometric Identification and Security

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Autonomous Detection and Evaluation of Deepfakes: A Comprehensive Study

Sunil, Reshma, Mer, Parita, Diwan, Anjali·2023·2023 Seventh International Conference on Image Information Processing (ICIIP)

Abstract

In recent times, there has been a notable advancement in deepfake techniques and the accessibility of extensive, cost-free databases. Consequently, even folks lacking technological expertise can now edit or produce visually authentic samples for various goals, both benign and harmful in nature. This paper provides a comprehensive analysis of the classification of methods used for deepfake generation and detection. The paper considers numerous factors, including the identified forgery, methodology or techniques used, evaluation metrics used for performance analysis, and the utilized dataset. By studying the development of deepfakes and the most up-to-date deepfake detecting methods, this study gives a full picture of deepfake techniques and makes it easier to come up with new, more reliable methods to fight the growing difficulty of deepfakes.

Overview

Integrity signals

Registry

Crossref · 10.1109/iciip61524.2023.10537789

Cites

4 of 4 references verified0 citation errors · 0 not checkable

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Recomputing p-values needs the paper’s full text.

Verification

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This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Citations

Cites

Show all 4 references

Research works printed but not linked: 34

  1. 10.1109/TCSVT.2022.3217950 · DOI
  2. 10.1109/GCAT55367.2022.9972081 · DOI
  3. 10.1109/tmm.2023.3237322 · DOI
  4. 10.1109/TBIOM.2022.3201887 · DOI
  5. 10.1109/JSTSP.2020.2999185 · DOI · Paper page
  6. 10.1109/ICNGIS54955.2022.10079802 · DOI
  7. 10.1109/ICIBA50161.2020.9277303 · DOI
  8. 10.1109/ICACRS55517.2022.10029095 · DOI
  9. 10.1109/TPAMI.2021.3093446 · DOI · Paper page
  10. 10.1109/I-SMAC55078.2022.9987265 · DOI
  11. 10.1109/IICSPI51290.2020.9332419 · DOI · Paper page
  12. 10.1109/ICOSST57195.2022.10016871 · DOI
  13. 10.1109/ICOSNIKOM56551.2022.10034890 · DOI
  14. 10.1109/TIFS.2020.3045937 · DOI
  15. 10.1109/iSES50453.2020.00031 · DOI · Paper page
  16. 10.1109/TNNLS.2022.3233063 · DOI
  17. 10.1109/TMM.2023.3237169 · DOI
  18. 10.1109/LSP.2022.3193590 · DOI
  19. 10.1109/ACCESS.2022.3215963 · DOI
  20. 10.1109/TIFS.2023.3290752 · DOI · Paper page
  21. 10.1109/TIFS.2022.3146781 · DOI
  22. 10.1109/TCSVT.2022.3209336 · DOI
  23. 10.1109/TMM.2023.3301242 · DOI
  24. 10.1109/TIFS.2023.3266702 · DOI
  25. 10.1109/TCSVT.2023.3281475 · DOI
  26. 10.1109/LSP.2022.3214768 · DOI · Paper page
  27. 10.1109/LSP.2022.3205562 · DOI
  28. 10.1109/INDICON49873.2020.9342252 · DOI · Paper page
  29. 10.1109/AIPR50011.2020.9425167 · DOI · Paper page
  30. 10.1049/ipr2.12105 · DOI
  31. 10.1109/AI4G50087.2020.9311067 · DOI · Paper page
  32. 10.1109/TTS.2020.3001312 · DOI · Paper page
  33. 10.1109/IJCNN48605.2020.9207034 · DOI
  34. 10.1109/ASPCON49795.2020.9276674 · DOI

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Metadata

Authors
Sunil, R., Mer, P., & Diwan, A.
Affiliations
Marwadi University (IN)
Venue
2023 Seventh International Conference on Image Information Processing (ICIIP)
Publisher
IEEE
Published
2023-11-22
Pages
35-40
OpenAlex
W4399111297
Cited by
4
References
4
Citations by year
2026: 1 · 2025: 2 · 2024: 1
FWCI
0.43
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Image Information Processing (ICIIP) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
Keywords
deepfakes, deepfake detection, deepfake generation, deepfake datasets

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