research-article

Detection of AI-Manipulated Fake Faces via Mining Generalized Features

Authors:

Yao ZhaoAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 4

Article No.: 94, Pages 1 - 23

https://doi.org/10.1145/3499026

Published: 04 March 2022 Publication History

Abstract

Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society. Although existing detection methods consider different categories of fake faces, the performance on detecting the fake faces with “unseen” manipulation techniques is still poor due to the distribution bias among cross-manipulation techniques. To solve this problem, we propose a novel framework that focuses on mining intrinsic features and further eliminating the distribution bias to improve the generalization ability. First, we focus on mining the intrinsic clues in the channel difference image (CDI) and spectrum image (SI) view of two different aspects, including the camera imaging process and the indispensable step in AI manipulation process. Then, we introduce the Octave Convolution and an attention-based fusion module to effectively and adaptively mine intrinsic features from CDI and SI view of these two different but intrinsic aspects. Finally, we design an alignment module to eliminate the bias of manipulation techniques to obtain a more generalized detection framework. We evaluate the proposed framework on four categories of fake faces datasets with the most popular and state-of-the-art manipulation techniques and achieve very competitive performances. We further conduct experiments on cross-manipulation techniques, and the results of our method show the superior advantages on improving generalization ability.

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Cited By

Liu HLiu XTan ZLi XZhao Y(2024)PADVG: A Simple Baseline of Active Protection for Audio-Driven Video GenerationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363855620:6(1-19)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3638556
Tang LYe QHu HXue QXiao YLi J(2024)DeepMark: A Scalable and Robust Framework for DeepFake Video DetectionACM Transactions on Privacy and Security10.1145/362997627:1(1-26)Online publication date: 5-Feb-2024
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Yu YNi RYang SZhao YKot A(2024)Narrowing Domain Gaps With Bridging Samples for Generalized Face Forgery DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.331034126(3405-3417)Online publication date: 1-Jan-2024
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Index Terms

Detection of AI-Manipulated Fake Faces via Mining Generalized Features
1. Applied computing
  1. Computer forensics
    1. Investigation techniques
    2. System forensics
2. Security and privacy
  1. Human and societal aspects of security and privacy

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 4

November 2022

497 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3514185

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 04 March 2022

Accepted: 01 November 2021

Revised: 01 September 2021

Received: 01 April 2021

Published in TOMM Volume 18, Issue 4

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Cited By

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https://dl.acm.org/doi/10.1145/3638556
Tang LYe QHu HXue QXiao YLi J(2024)DeepMark: A Scalable and Robust Framework for DeepFake Video DetectionACM Transactions on Privacy and Security10.1145/362997627:1(1-26)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1145/3629976
Yu YNi RYang SZhao YKot A(2024)Narrowing Domain Gaps With Bridging Samples for Generalized Face Forgery DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.331034126(3405-3417)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3310341
Zhou KSun GWang JWang JYu L(2024)FLAG: frequency-based local and global network for face forgery detectionMultimedia Tools and Applications10.1007/s11042-024-18751-6Online publication date: 28-Mar-2024
https://doi.org/10.1007/s11042-024-18751-6
Aieprasert TMahdlang YPansiri CSae-Bae NKhomkham B(2024)Transferability of CNN models for GAN-generated face detectionMultimedia Tools and Applications10.1007/s11042-024-18664-4Online publication date: 1-Mar-2024
https://doi.org/10.1007/s11042-024-18664-4
Kara IKorkmaz CKaratatar AAydos M(2023)A forensic method for investigating manipulated video recordingsComputer Fraud & Security10.12968/S1361-3723(23)70003-12023:1Online publication date: Jan-2023
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Cheng HGuo YWang TLi QChang XNie L(2023)Voice-Face Homogeneity Tells DeepfakeACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362523120:3(1-22)Online publication date: 10-Nov-2023
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Lin KHan WLi SGu ZZhao HMei Y(2023)Detecting Deepfake Videos Using Spatiotemporal Trident NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3623639Online publication date: 13-Sep-2023
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Zhang YChen NQi SXue MHua Z(2023)Detection of Recolored Image by Texture Features in Chrominance ComponentsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357107619:3(1-23)Online publication date: 25-Feb-2023
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Liu XYu YLi XZhao YGuo G(2023)TCSD: Triple Complementary Streams Detector for Comprehensive Deepfake DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355800419:6(1-22)Online publication date: 12-Jul-2023
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