research-article

Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

Authors:

Lorenzo Cavallaro,

Kui RenAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 7131 - 7142

https://doi.org/10.1145/3581783.3612386

Published: 27 October 2023 Publication History

Abstract

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues.

In this paper, we strive to address these shortcomings from three aspects: (1) We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts forgery evidence. (2) We devise three functional modules to handle the multi-stream and multi-scale features in a collaborative learning scheme. (3) Confronted with the challenge of obtaining forgery annotations, we propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations. Empirically, our method demonstrates significantly improved robustness and generalizability, outperforming previous methods on six benchmarks, and improving the frame-level AUC on Deepfake Detection Challenge preview dataset from 0.797 to 0.835 and video-level AUC on CelebDF_v1 dataset from 0.811 to 0.847. Our implementation is available at https://github.com/sccsok/Locate-and-Verify.

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Cited By

Thakral KAgarwal HNarayan KMittal SVatsa MSingh R(2025)DeePhyNet: Toward Detecting Phylogeny in DeepfakesIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2024.34874827:1(132-145)Online publication date: Jan-2025
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Zheng HChen JLiu TCheng YWang ZWang YGao LJi SZhang X(2024)DP-Poison: Poisoning Federated Learning under the Cover of Differential PrivacyACM Transactions on Privacy and Security10.1145/370232528:1(1-28)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1145/3702325
Liu PTao QZhou JFan SWang ZShao RBai SZhu HNie LSatoh S(2024)Robust Deepfake Detection by Addressing Generalization and Trustworthiness Challenges: A Short SurveyProceedings of the 1st ACM Multimedia Workshop on Multi-modal Misinformation Governance in the Era of Foundation Models10.1145/3689090.3689386(3-11)Online publication date: 28-Oct-2024
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Index Terms

Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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the National Natural Science Foundation of China
the National Key R\&D Program of China
the Key R\&D Program of Zhejiang Province

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MM '23

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SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Thakral KAgarwal HNarayan KMittal SVatsa MSingh R(2025)DeePhyNet: Toward Detecting Phylogeny in DeepfakesIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2024.34874827:1(132-145)Online publication date: Jan-2025
https://doi.org/10.1109/TBIOM.2024.3487482
Zheng HChen JLiu TCheng YWang ZWang YGao LJi SZhang X(2024)DP-Poison: Poisoning Federated Learning under the Cover of Differential PrivacyACM Transactions on Privacy and Security10.1145/370232528:1(1-28)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1145/3702325
Liu PTao QZhou JFan SWang ZShao RBai SZhu HNie LSatoh S(2024)Robust Deepfake Detection by Addressing Generalization and Trustworthiness Challenges: A Short SurveyProceedings of the 1st ACM Multimedia Workshop on Multi-modal Misinformation Governance in the Era of Foundation Models10.1145/3689090.3689386(3-11)Online publication date: 28-Oct-2024
https://doi.org/10.1145/3689090.3689386
Wu SChen HYin YHu SFeng RJiao YYang ZLiu ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Joint-Motion Mutual Learning for Pose Estimation in VideoProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681179(8962-8971)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681179
Li ZTeng ZZhang BFan JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Boosting Non-causal Semantic Elimination: An Unconventional Harnessing of LVM for Open-World Deepfake InterpretationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680737(3528-3537)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680737
Xia RZhou DLiu DYuan LWang SLi JWang NGao XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Advancing Generalized Deepfake Detector with Forgery Perception GuidanceProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680713(6676-6685)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680713
Xu JChen JSong XHan FShan HJiang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Identity-Driven Multimedia Forgery Detection via Reference AssistanceProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680622(3887-3896)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680622
Lin CYi FWang HDeng JZhao ZLi QShen C(2024)Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.346146919(8832-8844)Online publication date: 1-Jan-2024
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Zhang XXu HBa ZWang ZHong YLiu JQin ZRen K(2024)PrivacyAsst: Safeguarding User Privacy in Tool-Using Large Language Model AgentsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337277721:6(5242-5258)Online publication date: Nov-2024
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Zhang RHe PLi HWang SCao Y(2024)Temporal Diversified Self-Contrastive Learning for Generalized Face Forgery DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.343655434:12(12782-12795)Online publication date: Dec-2024
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