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DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

Authors:

Felix Juefei-Xu,

Jianjun ZhaoAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 4318 - 4327

https://doi.org/10.1145/3394171.3413707

Published: 12 October 2020 Publication History

Abstract

As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake detection technique that exposes DeepFakes by monitoring the heartbeat rhythms. DeepRhythm utilizes dual-spatial-temporal attention to adapt to dynamically changing face and fake types. Extensive experiments on FaceForensics++ and DFDC-preview datasets have confirmed our conjecture and demonstrated not only the effectiveness, but also the generalization capability of DeepRhythm over different datasets by various DeepFakes generation techniques and multifarious challenging degradations.

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Index Terms

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

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Published: 12 October 2020

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MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

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https://doi.org/10.3390/electronics13091749
Xu YTerhörst PPedersen MRaja K(2024)Analyzing Fairness in Deepfake Detection With Massively Annotated DatabasesIEEE Transactions on Technology and Society10.1109/TTS.2024.33654215:1(93-106)Online publication date: Mar-2024
https://doi.org/10.1109/TTS.2024.3365421
Liu JXie JWang YZha Z(2024)Adaptive Texture and Spectrum Clue Mining for Generalizable Face Forgery DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334429319(1922-1934)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3344293
Luo AKong CHuang JHu YKang XKot A(2024)Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.333221819(1168-1182)Online publication date: 2024
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Zhang DFu CLu DLi JZhang Y(2024)Bi-Source Reconstruction-Based Classification Network for Face Forgery Video DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.333039034:6(4257-4269)Online publication date: Jun-2024
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Gao JXia ZMarcialis GDang CDai JFeng X(2024)DeepFake detection based on high-frequency enhancement network for highly compressed contentExpert Systems with Applications10.1016/j.eswa.2024.123732249(123732)Online publication date: Sep-2024
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