tutorial

Deepfake Detection: A Tutorial

Authors:

Md Shohel Rana,

Andrew H. SungAuthors Info & Claims

IWSPA '23: Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics

Pages 55 - 56

https://doi.org/10.1145/3579987.3586562

Published: 24 April 2023 Publication History

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Abstract

This tutorial presents developments on the detection of Deepfakes, which are realistic images, audios and videos created using deep learning techniques. Deepfakes can be readily used for malicious purposes and pose a serious threat to privacy and security. The tutorial summarizes recent Deepfake detection techniques and evaluates their effectiveness with respect to several benchmark datasets. Our study finds that no single method can reliably detect all Deepfakes and, therefore, combining multiple methods is often necessary to achieve high detection rates. The study also suggests that more extensive and diverse datasets are needed to improve the accuracy of detection algorithms. A taxonomy of Deepfake detection techniques is introduced to aid future research and development in the field. We conclude by calling for the development of more effective Deepfake detection methods and countermeasures to combat this evolving and spreading threat.

References

[1]

Jon Bateman. 2020. Fakeapp. Webpage. (Aug. 2020). Retrieved March 11, 2023 from https://carnegieendowment.org/2020/08/10/get-ready-for-deepfakes-t o-be-used-in-financial-scams-pub-82469.

Google Scholar

[2]

2017. Faceapp. Application. (2017). Retrieved March 11, 2023 from https://ww w.faceapp.com/.

Google Scholar

[3]

2018. Fakeapp. Application. (2018). Retrieved March 11, 2023 from https://ww w.malavida.com/en/soft/fakeapp/.

Google Scholar

[4]

Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. 2020. Celeb-df: a large-scale challenging dataset for deepfake forensics. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3204--3213.

Crossref

Google Scholar

[5]

Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep learning face attributes in the wild. In 2015 IEEE International Conference on Computer Vision (ICCV), 3730--3738.

Digital Library

Google Scholar

[6]

Md Shohel Rana, Mohammad Nur Nobi, Beddhu Murali, and Andrew H. Sung. 2022. Deepfake detection: a systematic literature review. IEEE Access, 10, 25494-- 25513.

Crossref

Google Scholar

[7]

Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Niessner. 2019. Faceforensics: learning to detect manipulated facial images. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 1--11.

Crossref

Google Scholar

[8]

Supasorn Suwajanakorn, Steven M. Seitz, and Ira Kemelmacher-Shlizerman. 2017. Synthesizing obama: learning lip sync from audio. ACM Trans. Graph., 36, 4, Article 95, 13 pages.

Digital Library

Google Scholar

[9]

Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Niessner. 2018. Face2face: real-time face capture and reenactment of rgb videos. Commun. ACM, 62, 1, 96--104.

Digital Library

Google Scholar

[10]

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV), 2242--2251.

Crossref

Google Scholar

Cited By

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Umbach RHenry NBeard GBerryessa C(2024)Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 CountriesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642382(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642382
Rana MNur Nobi MSung A(2024)DeepDistAL: Deepfake Dataset Distillation using Active Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00768(7723-7730)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00768
Reddy PReddy PSiddartha BAyush BKulkarni VRout S(2024)Enhancing Deepfake Detection with Advanced Deep Learning Algorithms2024 6th International Conference on Computational Intelligence and Networks (CINE)10.1109/CINE63708.2024.10881416(1-6)Online publication date: 19-Dec-2024
https://doi.org/10.1109/CINE63708.2024.10881416

Index Terms

Deepfake Detection: A Tutorial
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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Published In

IWSPA '23: Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics

April 2023

107 pages

ISBN:9798400700996

DOI:10.1145/3579987

General Chairs:
David Marchette
NSWC, USA
,
Andrew Sung
University of Southern Mississippi, USA
,
Rakesh M. Verma
University of Houston, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 24 April 2023

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CODASPY '23

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SIGSAC

CODASPY '23: Thirteenth ACM Conference on Data and Application Security and Privacy

April 26, 2023

NC, Charlotte, USA

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Umbach RHenry NBeard GBerryessa C(2024)Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 CountriesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642382(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642382
Rana MNur Nobi MSung A(2024)DeepDistAL: Deepfake Dataset Distillation using Active Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00768(7723-7730)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00768
Reddy PReddy PSiddartha BAyush BKulkarni VRout S(2024)Enhancing Deepfake Detection with Advanced Deep Learning Algorithms2024 6th International Conference on Computational Intelligence and Networks (CINE)10.1109/CINE63708.2024.10881416(1-6)Online publication date: 19-Dec-2024
https://doi.org/10.1109/CINE63708.2024.10881416

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GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

PRNU-based Deepfake Detection

Audio-deepfake detection: Adversarial attacks and countermeasures

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