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Fighting AI-synthesized Fake Media

Published: 20 October 2021 Publication History

Abstract

Recent years have witnessed an unexpected and astonishing rise of AI-synthesized fake media (GAN synthesized faces, face-swap videos, and style transferred audios, commonly known as the DeepFakes), thanks to the rapid advancement of technology and the omnipresence of social media. Together with other forms of online disinformation, the AI-synthesized fake media are eroding our trust in online information and have already caused real damage. It is thus important to develop countermeasures to limit the negative impacts of AI-synthesized fake media. In this presentation, I will first provide a high level overview of the AI-synthesized fake media and their potential negative social impacts. I will then highlight recent technical developments to fight AI-synthesized fake media, including the signal level methods [1], and physical/physiological based methods [2,3], as well as our effort of making large datasets [4] and open-platforms for DeepFake detection [5]. I will also point out some current challenges, and discuss the future of AI-synthesized fake media and their counter technology.

References

[1]
Y. Li, M.-C. Chang, and S. Lyu, "In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking," in IEEE Workshop on Information Forensics and Security (WIFS), (Hong Kong), December 2018.
[2]
X. Yang, Y. Li, and S. Lyu, "Exposing deep fakes using inconsistent head poses," in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Bristol, United Kingdom), 2019.
[3]
S. Hu, Y. Li, and S. Lyu, "Exposing GAN-generated faces using inconsistent corneal specular highlights," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, 2021.
[4]
Y. Li, P. Sun, H. Qi, and S. Lyu, "Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics," in IEEE Conference on Computer Vision and Patten Recognition (CVPR), (Seattle, WA, United States), 2020.
[5]
Yuezun Li, Cong Zhang, Pu Sun, Lipeng Ke, Yan Ju, Honggang Qi and Siwei Lyu. DeepFake-o-meter: An Open Platform for DeepFake Detection. In International Conference on Systematic Approaches to Digital Forensic Engineering (SADFE), 2021.

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Published In

cover image ACM Conferences
ADGD '21: Proceedings of the 1st Workshop on Synthetic Multimedia - Audiovisual Deepfake Generation and Detection
October 2021
39 pages
ISBN:9781450386821
DOI:10.1145/3476099
  • Program Chairs:
  • Stefan Winkler,
  • Weiling Chen,
  • Abhinav Dhall,
  • Pavel Korshunov
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2021

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Author Tags

  1. deepfakes
  2. multimedia forensics

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  • Keynote

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MM '21
Sponsor:
MM '21: ACM Multimedia Conference
October 24, 2021
Virtual Event, China

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