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FMFCC-V: An Asian Large-Scale Challenging Dataset for DeepFake Detection

Authors:

Zeyu ZhangAuthors Info & Claims

IH&MMSec '22: Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security

Pages 7 - 18

https://doi.org/10.1145/3531536.3532946

Published: 23 June 2022 Publication History

Abstract

The abuse of DeepFake technique has raised enormous public concerns in recent years. Currently, the existing DeepFake datasets suffer some weaknesses of obvious visual artifacts, minimal Asian proportion, backward synthesis methods and short video length. To make up these weaknesses, we have constructed an Asian large-scale challenging DeepFake dataset to enable the training of DeepFake detection models and organized the accompanying video track of the first Fake Media Forensics Challenge of China Society of Image and Graphics (FMFCC-V). The FMFCC-V dataset is by far the first and the largest public available Asian dataset for DeepFake detection, which contains 38102 DeepFake videos and 44290 pristine videos, corresponding more than 23 million frames. The source videos in the FMFCC-V dataset are carefully collected from 83 paid individuals and all of them are Asians. The DeepFake videos are generated by four of the most popular face swapping methods. Extensive perturbations are applied to obtain a more challenging benchmark of higher diversity. The FMFCC-V dataset can lend powerful support to the development of more effective DeepFake detection methods. We contribute a comprehensive evaluation of six representative DeepFake detection methods to demonstrate the level of challenge posed by FMFCC-V dataset. Meanwhile, we provide a detailed analysis of the top submissions from the FMFCC-V competition.

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Cited By

Peng CChen TLiu DGuo HWang NGao X(2025)Revisiting face forgery detection towards generalizationNeural Networks10.1016/j.neunet.2025.107310(107310)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2025.107310
Akhtar ZPendyala TAthmakuri V(2024)Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the CurveForensic Sciences10.3390/forensicsci40300214:3(289-377)Online publication date: 13-Jul-2024
https://doi.org/10.3390/forensicsci4030021
She HHu YLiu BLi JLi C(2024)Using Graph Neural Networks to Improve Generalization Capability of the Models for Deepfake DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.345135619(8414-8427)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3451356
Show More Cited By

Index Terms

FMFCC-V: An Asian Large-Scale Challenging Dataset for DeepFake Detection
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases
2. Security and privacy
  1. Software and application security
    1. Social network security and privacy

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Published In

cover image ACM Conferences

IH&MMSec '22: Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security

June 2022

177 pages

ISBN:9781450393553

DOI:10.1145/3531536

General Chair:
B.S. Manjunath
UC Santa Barbara, California, USA
,
Program Chairs:
Jan Butora
University of Lille, France
,
Benedetta Tondi
University of Siena, Italy
,
Claus Vielhauer
Brandenburg University of Applied Sciences, Germany

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 23 June 2022

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National Key Technology Reseach and Development Program

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IH&MMSec '22

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SIGMM

IH&MMSec '22: ACM Workshop on Information Hiding and Multimedia Security

June 27 - 28, 2022

CA, Santa Barbara, USA

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Cited By

Peng CChen TLiu DGuo HWang NGao X(2025)Revisiting face forgery detection towards generalizationNeural Networks10.1016/j.neunet.2025.107310(107310)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2025.107310
Akhtar ZPendyala TAthmakuri V(2024)Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the CurveForensic Sciences10.3390/forensicsci40300214:3(289-377)Online publication date: 13-Jul-2024
https://doi.org/10.3390/forensicsci4030021
She HHu YLiu BLi JLi C(2024)Using Graph Neural Networks to Improve Generalization Capability of the Models for Deepfake DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.345135619(8414-8427)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3451356
Liang HLeng YLuo JChen JGuo X(2024)A Face Forgery Video Detection Model Based on Knowledge Distillation2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)10.1109/SNPD61259.2024.10673906(50-55)Online publication date: 5-Jul-2024
https://doi.org/10.1109/SNPD61259.2024.10673906
Waseem SAbu Bakar SAhmed BOmar ZEisa TDalam M(2023)DeepFake on Face and Expression Swap: A ReviewIEEE Access10.1109/ACCESS.2023.332440311(117865-117906)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3324403
Rehaan MKaur NKingra S(2023)Face manipulated deepfake generation and recognition approaches: a surveySmart Science10.1080/23080477.2023.226838012:1(53-73)Online publication date: 30-Oct-2023
https://doi.org/10.1080/23080477.2023.2268380

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