research-article

Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN)

Authors:

Kun Yu,

Hui XueAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 88 - 89

https://doi.org/10.1145/3366424.3382711

Published: 20 April 2020 Publication History

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Abstract

In this paper, we propose a novel Patch&Pair Convolutional Neural Networks (PPCNN) to distinguish Deepfake videos or images from real ones. Through the comprehensive evaluations on public datasets, we demonstrate that our model performs better than existing detection methods and show better generalization.

References

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Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. 2018. Mesonet: a compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 1–7.

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Pavel Korshunov and Sébastien Marcel. 2018. Deepfakes: a new threat to face recognition? assessment and detection. arXiv preprint arXiv:1812.08685(2018).

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Yuezun Li and Siwei Lyu. 2018. Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656 2 (2018).

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Huy H Nguyen, Junichi Yamagishi, and Isao Echizen. 2019. Capsule-forensics: Using capsule networks to detect forged images and videos. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2307–2311.

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Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2019. FaceForensics++: Learning to Detect Manipulated Facial Images. In International Conference on Computer Vision (ICCV).

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Cited By

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Shen MTan ZNiyato DLiu YKang JXiong ZZhu LWang WShen X(2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657284
Vigneshwaran TVelammal B(2024)A Comprehensive Survey on Deep Learning Techniques for Digital Video ForensicsJournal of Information & Knowledge Management10.1142/S021964922450034523:03Online publication date: 21-Mar-2024
https://doi.org/10.1142/S0219649224500345
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
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Index Terms

Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN)
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information

Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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 ACM 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: 20 April 2020

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SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Shen MTan ZNiyato DLiu YKang JXiong ZZhu LWang WShen X(2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657284
Vigneshwaran TVelammal B(2024)A Comprehensive Survey on Deep Learning Techniques for Digital Video ForensicsJournal of Information & Knowledge Management10.1142/S021964922450034523:03Online publication date: 21-Mar-2024
https://doi.org/10.1142/S0219649224500345
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
Kandari MTripathi VPant BSar AChoudhury TChoudhury T(2024)Detecting Deepfake Videos Through CNN-MLP Model in Media Forensics2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10687433(1-7)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10687433
Liu QXue ZLiu HLiu J(2024)Enhancing Deepfake Detection With Diversified Self-Blending Images and ResidualsIEEE Access10.1109/ACCESS.2024.338219612(46109-46117)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3382196
Sharma VGarg RCaudron Q(2024)A systematic literature review on deepfake detection techniquesMultimedia Tools and Applications10.1007/s11042-024-19906-1Online publication date: 2-Aug-2024
https://doi.org/10.1007/s11042-024-19906-1
Sandotra NArora B(2024)A comprehensive evaluation of feature-based AI techniques for deepfake detectionNeural Computing and Applications10.1007/s00521-023-09288-036:8(3859-3887)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09288-0
Rajput TArora B(2024)A Systematic Review of Deepfake Detection Using Learning Techniques and Vision TransformerProceedings of Fifth International Conference on Computing, Communications, and Cyber-Security10.1007/978-981-97-2550-2_17(217-235)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-981-97-2550-2_17
Volkova S(2023)A Method for Deepfake Detection Using Convolutional Neural NetworksScientific and Technical Information Processing10.3103/S014768822305014350:5(475-485)Online publication date: 1-Dec-2023
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Falduti MTessaris S(2023)Mapping the Interdisciplinary Research on Non-consensual Pornography: Technical and Quantitative PerspectivesDigital Threats: Research and Practice10.1145/36084834:3(1-22)Online publication date: 6-Oct-2023
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Convolutional neural networks for wavelet domain super resolution

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