research-article

Am I a Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs under Deepfake Impersonation Attack

Authors:

Simon S. WooAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 512 - 523

https://doi.org/10.1145/3485447.3512212

Published: 25 April 2022 Publication History

Abstract

Recent advancements in web-based multimedia technologies, such as face recognition web services powered by deep learning, have been significant. As a result, companies such as Microsoft, Amazon, and Naver provide highly accurate commercial face recognition web services for a variety of multimedia applications. Naturally, such technologies face persistent threats, as virtually anyone with access to deepfakes can quickly launch impersonation attacks. These attacks pose a serious threat to authentication services, which rely heavily on the performance of their underlying face recognition technologies. Despite its gravity, deepfake abuse involving commercial web services and their robustness have not been thoroughly measured and investigated. By conducting a case study on celebrity face recognition, we examine the robustness of black-box commercial face recognition web APIs and open-source tools against Deepfake Impersonation (DI) attacks. While the majority of APIs do not make specific claims of deepfake robustness, we find that authentication mechanisms may get built one top of them, nonetheless. We demonstrate the vulnerability of face recognition technologies to DI attacks, achieving respective success rates of 78.0% for targeted (TA) attacks; we also propose mitigation strategies, lowering respective attack success rates to as low as 1.26% for TA attacks with adversarial training.

References

[1]

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.

[2]

Shruti Agarwal, Tarek El-Gaaly, Hany Farid, and Ser-Nam Lim. 2020. Detecting Deep-Fake Videos from Appearance and Behavior. arXiv preprint arXiv:2004.14491(2020).

[3]

Henry Ajder, Giorgio Patrini, Francesco Cavalli, and Laurence Cullen. 2019. The state of deepfakes: Landscape, threats, and impact. Amsterdam: Deeptrace(2019).

[4]

Marinus Analytics. 2019. Traffic Jam was used to identify an estimated 3,800 victims of sex trafficking. Retrieved October-14-2020 from https://www.marinusanalytics.com/traffic-jam

[5]

Google Cloud Vision API. 2020. celebrity recognition. Retrieved October-14-2020 from https://cloud.google.com/vision/docs/celebrity-recognition

[6]

Microsoft Azure. 2020. Cognitive Services - Face API - An AI service that analyzes faces in images. Retrieved August-25-2021 from https://azure.microsoft.com/en-us/services/cognitive-services/face/#features

[7]

Baidu. 2020. Baidu AI Open Platform. Retrieved October-14-2020 from https://ai.baidu.com/

[8]

Enrico Bergamo. 2019. Authenticate applications through facial recognition with Amazon Cognito and Amazon Rekognition. Retrieved August-25-2021 from https://aws.amazon.com/blogs/developer/authenticate-applications-through-facial-recognition-with-amazon-cognito-and-amazon-rekognition

[9]

Nicholas Carlini and Hany Farid. 2020. Evading deepfake-image detectors with white-and black-box attacks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 658–659.

[10]

Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Daun, John Dickerson, Gavin Taylor, and Tom Goldstein. 2020. LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. In NIPS 2020 Workshop on Dataset Curation and Security.

[11]

François Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1251–1258.

[12]

J. S. Chung, A. Nagrani, and A. Zisserman. 2018. VoxCeleb2: Deep Speaker Recognition. In INTERSPEECH.

[13]

Naver Clova. 2021. CLOVA Face Recognition (CFR) - Celebrity Face Recognition API. Retrieved December-02-2021 from https://www.ncloud.com/product/aiService/cfr

[14]

Samantha Cole. 2020. This Open-Source Program Deepfakes You During Zoom Meetings, in Real Time. Retrieved January-5-2021 from https://www.vice.com/en/article/g5xagy/this-open-source-program-deepfakes-you-during-zoom-meetings-in-real-time

[15]

Jeffery DelViscio. 2020. A Nixon Deepfake, a ‘Moon Disaster’ Speech and an Information Ecosystem at Risk - Scientific American. Retrieved July-23-2020 from https://www.scientificamerican.com/article/a-nixon-deepfake-a-moon-disaster-speech-and-an-information-ecosystem-at-risk1

[16]

Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4690–4699.

[17]

Li Deng, Geoffrey Hinton, and Brian Kingsbury. 2013. New types of deep neural network learning for speech recognition and related applications: An overview. In 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 8599–8603.

[18]

Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram, and Cristian Canton Ferrer. 2019. The Deepfake Detection Challenge (DFDC) Preview Dataset. arXiv preprint arXiv:1910.08854(2019).

[19]

Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. 2019. Efficient decision-based black-box adversarial attacks on face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7714–7722.

[20]

Face++. 2021. Face Comparing – Protection against Fake Face Attacks – Seasoned Solution – Accurate in Real World. Retrieved March-16-2021 from https://www.faceplusplus.com/face-comparing

[21]

FaceApp. 2018. FaceApp - Free Neural Face Transformation Filters. https://l.faceapp.com/?next=faceapp

[22]

Facebook, Inc.2021. What is the face recognition setting on Facebook and how does it work?Retrieved January-18-2021 from https://www.facebook.com/help/122175507864081

[23]

FaceSwapDevs. 2021. Deepfakes faceswap - GitHub Repository. https://github.com/deepfakes/faceswap. Accessed: April-7-2021.

[24]

Yun Fu, Guodong Guo, and Thomas S Huang. 2010. Age synthesis and estimation via faces: A survey. IEEE transactions on pattern analysis and machine intelligence 32, 11(2010), 1955–1976.

[25]

Teddy Furon. 2020. How to detect deepfakes using media forensics techniques. Retrieved April-7-2021 from https://blog.imatag.com/how-to-detect-deepfakes-using-media-forensics-techniques

[26]

Gabriela Galindo. 2020. XR Belgium posts deepfake of Belgian premier linking Covid-19 with climate crisis - The Brussels Times. Retrieved January-5-2021 from https://www.brusselstimes.com/news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis

[27]

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572(2014).

[28]

Joon Kuy Han, Hyoungshick Kim, and Simon S Woo. 2019. Nickel to Lego: Using Foolgle to Create Adversarial Examples to Fool Google Cloud Speech-to-Text API. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 2593–2595.

Digital Library

[29]

Tech HQ. 2019. Deepfakes are coming for your LinkedIn feed. Retrieved 1-21-2021 from https://techhq.com/2019/11/deepfakes-are-coming-for-your-linkedin-feed/l

[30]

Shehzeen Hussain, Paarth Neekhara, Malhar Jere, Farinaz Koushanfar, and Julian McAuley. 2021. Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 3348–3357.

[31]

Hyeonseong Jeon, Youngoh Bang, and Simon S Woo. 2019. Faketalkerdetect: Effective and practical realistic neural talking head detection with a highly unbalanced dataset. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 0–0.

[32]

Peter A Johnson, Bozhao Tan, and Stephanie Schuckers. 2010. Multimodal fusion vulnerability to non-zero effort (spoof) imposters. In 2010 IEEE International Workshop on Information Forensics and Security. IEEE, 1–5.

[33]

Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2021. Alias-free generative adversarial networks. Advances in Neural Information Processing Systems 34 (2021).

[34]

Hasam Khalid, Minha Kim, Shahroz Tariq, and Simon S Woo. 2021. Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors. In Proceedings of the 1st Workshop on Synthetic Multimedia-Audiovisual Deepfake Generation and Detection. 7–15.

Digital Library

[35]

Hasam Khalid, Shahroz Tariq, Minha Kim, and Simon S Woo. 2021. FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset. arXiv preprint arXiv:2108.05080(2021).

[36]

Halle Kiefer. 2018. Scarlett Johansson Ruminates on All the Deepfake Porn You Perverts Have Created From Her Image. Retrieved July-30-2020 from https://www.vulture.com/2018/12/scarlett-johansson-ruminates-on-deepfake-porn-of-her-image.html

[37]

Jeongho Kim, Shahroz Tariq, and Simon S Woo. 2022. PTD: Privacy-Preserving Human Face Processing Framework using Tensor Decomposition. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 1296–1303. https://doi.org/10.1145/3477314.3507036

Digital Library

[38]

Keeyoung Kim and Simon S Woo. 2018. When George Clooney Is Not George Clooney: Using GenAttack to Deceive Amazon’s and Naver’s Celebrity Recognition APIs. In IFIP International Conference on ICT Systems Security and Privacy Protection. Springer, 355–369.

[39]

Minha Kim, Shahroz Tariq, and Simon S Woo. 2021. Cored: Generalizing fake media detection with continual representation using distillation. In Proceedings of the 29th ACM International Conference on Multimedia. 337–346.

Digital Library

[40]

Minha Kim, Shahroz Tariq, and Simon S Woo. 2021. FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1001–1012.

[41]

Marek Kowalski. 2016. FaceSwap - GitHub Repository. https://github.com/MarekKowalski/FaceSwap. Accessed: 2020-02-12.

[42]

Sangyup Lee, Shahroz Tariq, Junyaup Kim, and Simon S Woo. 2021. TAR: Generalized Forensic Framework to Detect Deepfakes Using Weakly Supervised Learning. In IFIP International Conference on ICT Systems Security and Privacy Protection. Springer, 351–366.

[43]

Sangyup Lee, Shahroz Tariq, Youjin Shin, and Simon S Woo. 2021. Detecting handcrafted facial image manipulations and GAN-generated facial images using Shallow-FakeFaceNet. Applied Soft Computing 105 (2021), 107256.

Digital Library

[44]

Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. 2019. Celeb-df: A new dataset for deepfake forensics. arXiv preprint arXiv:1909.12962(2019).

[45]

Vineet Mehta, Parul Gupta, Ramanathan Subramanian, and Abhinav Dhall. 2021. FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios. In 26th International Conference on Intelligent User Interfaces. 61–63.

[46]

Yisroel Mirsky and Wenke Lee. 2020. The Creation and Detection of Deepfakes: A Survey. arXiv preprint arXiv:2004.11138(2020).

[47]

A. Nagrani, J. S. Chung, and A. Zisserman. 2017. VoxCeleb: a large-scale speaker identification dataset. In INTERSPEECH.

[48]

Kara Nesvig. 2020. Skai Jackson Used FaceApp to Morph With Zendaya, Harry Styles. Retrieved July-30-2020 from https://www.teenvogue.com/story/skai-jackson-faceapp-morph-zendaya-harry-styles

[49]

Omkar M. Parkhi, Andrea Vedaldi, and Andrew Zisserman. 2015. Deep Face Recognition. In British Machine Vision Conference.

[50]

Giorgio Patrini. 2019. Mapping the Deepfake Landscape - Deeptrace. Retrieved January-18-2022 from https://www.linkedin.com/pulse/mapping-deepfake-landscape-giorgio-patrini

[51]

Dan Patterson. 2020. President’s words used to create ”deepfakes” at Davos. Video. Retrieved July-23-2020 from https://www.cbsnews.com/video/presidents-words-used-to-create-deepfakes-at-davos

[52]

Shyam Purkayastha. 2019. Machine Learning Use Case: Facial Recognition Using Amazon Rekognition. Retrieved October-14-2020 from https://radiostud.io/machine-learning-use-case-facial-recognition-using-amazon-rekognition/

[53]

Amazon Web Service Amazon Rekognition. 2020. Comparing Faces in Images. Retrieved October-19-2020 from https://docs.aws.amazon.com/rekognition/latest/dg/faces-comparefaces.html

[54]

Aja Romano. 2019. New deepfakes research finds they’re mainly used to degrade women. Retrieved July-23-2020 from https://www.vox.com/2019/10/7/20902215/deepfakes-usage-youtube-2019-deeptrace-research-report

[55]

Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2019. FaceForensics++: Learning to Detect Manipulated Facial Images. In ICCV 2019.

[56]

Amazon Web Services. 2020. Amazon Rekognition - Automate your image and video analysis with machine learning. Retrieved October-14-2020 from https://aws.amazon.com/rekognition

[57]

Amazon Web Services. 2020. Use cases that involve public safety. Retrieved October-14-2020 from https://docs.aws.amazon.com/rekognition/latest/dg/considerations-public-safety-use-cases.html

[58]

Amazon Web Services. 2021. Amazon Rekognition Customers. Retrieved April-1-2021 from https://aws.amazon.com/rekognition/customers

[59]

Amazon Web Services. 2021. The Facts on Facial Recognition with Artificial Intelligence. Retrieved April-1-2021 from https://aws.amazon.com/rekognition/the-facts-on-facial-recognition-with-artificial-intelligence

[60]

Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. 2019. First Order Motion Model for Image Animation. ArXiv abs/2003.00196(2019).

[61]

Shahroz Tariq, Sangyup Lee, Hoyoung Kim, Youjin Shin, and Simon S Woo. 2018. Detecting both machine and human created fake face images in the wild. In Proceedings of the 2nd international workshop on multimedia privacy and security. 81–87.

Digital Library

[62]

Shahroz Tariq, Sangyup Lee, Hoyoung Kim, Youjin Shin, and Simon S Woo. 2019. GAN is a friend or foe? a framework to detect various fake face images. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 1296–1303.

Digital Library

[63]

Shahroz Tariq, Sangyup Lee, and Simon Woo. 2021. One detector to rule them all: Towards a general deepfake attack detection framework. In Proceedings of the Web Conference 2021. 3625–3637.

Digital Library

[64]

Shahroz Tariq, Sangyup Lee, and Simon S Woo. 2020. A Convolutional LSTM based Residual Network for Deepfake Video Detection. arXiv preprint arXiv:2009.07480(2020).

[65]

Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1–12.

Digital Library

[66]

Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. 2018. Face2Face: Real-time Face Capture and Reenactment of RGB Videos. Commun. ACM 62, 1 (Dec. 2018), 96–104. https://doi.org/10.1145/3292039

Digital Library

[67]

NY Times. 2020. Zoombombing: When Video Conferences Go Wrong. Retrieved 1-21-2021 from https://www.nytimes.com/2020/03/20/style/zoombombing-zoom-trolling.html

[68]

The Verge. 2019. A spy reportedly used an AI-generated profile picture to connect with sources on LinkedIn. Retrieved 1-21-2021 from https://www.theverge.com/2019/6/13/18677341/ai-generated-fake-faces-spy-linked-in-contacts-associated-press

[69]

James Vincent. 2018. Watch Jordan Peele use AI to make Barack Obama deliver a PSA about fake news. Retrieved July-30-2020 from https://www.theverge.com/tldr/2018/4/17/17247334/ai-fake-news-video-barack-obama-jordan-peele-buzzfeed

[70]

Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. 2018. Deep learning for computer vision: A brief review. Computational intelligence and neuroscience 2018 (2018).

[71]

Run Wang, Lei Ma, Felix Juefei-Xu, Xiaofei Xie, Jian Wang, and Yang Liu. 2019. FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces. arXiv preprint arXiv:1909.06122(2019).

[72]

Qixue Xiao, Yufei Chen, Chao Shen, Yu Chen, and Kang Li. 2019. Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 443–460. https://www.usenix.org/conference/usenixsecurity19/presentation/xiao

[73]

Chaofei Yang, Leah Ding, Yiran Chen, and Hai Li. 2021. Defending against gan-based deepfake attacks via transformation-aware adversarial faces. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.

[74]

Ziqi Yang, Jiyi Zhang, Ee-Chien Chang, and Zhenkai Liang. 2019. Neural network inversion in adversarial setting via background knowledge alignment. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 225–240.

Digital Library

[75]

Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine 13, 3 (2018), 55–75.

[76]

Honggang Yu, Kaichen Yang, Teng Zhang, Yun-Yun Tsai, Tsung-Yi Ho, and Yier Jin. 2020. Cloudleak: Large-scale deep learning models stealing through adversarial examples. In Proceedings of Network and Distributed Systems Security Symposium (NDSS).

[77]

Kan Yuan, Di Tang, Xiaojing Liao, XiaoFeng Wang, Xuan Feng, Yi Chen, Menghan Sun, Haoran Lu, and Kehuan Zhang. 2019. Stealthy porn: Understanding real-world adversarial images for illicit online promotion. In 2019 IEEE Symposium on Security and Privacy (SP). IEEE, 952–966.

[78]

Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, and Victor Lempitsky. 2019. Few-shot adversarial learning of realistic neural talking head models. In Proceedings of the IEEE International Conference on Computer Vision. 9459–9468.

[79]

Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao. 2016. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters 23, 10 (2016), 1499–1503.

[80]

Yaoyao Zhong and Weihong Deng. 2021. Towards Transferable Adversarial Attack Against Deep Face Recognition. IEEE Transactions on Information Forensics and Security 16 (2021), 1452–1466. https://doi.org/10.1109/tifs.2020.3036801

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Am I a Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs under Deepfake Impersonation Attack

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Samrouth KEl Housseini PDeforges O(2025)Siamese Network-Based Detection of Deepfake Impersonation Attacks with a Person of Interest ApproachACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370835221:3(1-23)Online publication date: 19-Feb-2025
https://doi.org/10.1145/3708352
Upadhayay BSahu D(2024)Fake Image Detection: A Comprehensive ReviewSSRN Electronic Journal10.2139/ssrn.4779382Online publication date: 2024
https://doi.org/10.2139/ssrn.4779382
Alam IMuneer MWoo SSerra ESpezzano F(2024)UGAD: Universal Generative AI Detector utilizing Frequency FingerprintsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680085(4332-4340)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680085
Zhou LMa CWang ZZhang YShi XWu L(2024)Robust Frame-Level Detection for Deepfake Videos With Lightweight Bayesian Inference WeightingIEEE Internet of Things Journal10.1109/JIOT.2023.333712811:7(13018-13028)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JIOT.2023.3337128
Tariq RHeo MWoo STariq S(2024)Beyond the Screen: Evaluating Deepfake Detectors under Moiré Pattern Effects2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00446(4429-4439)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00446
Zeng HYue ZShang LZhang YWang DElkind E(2023)On adversarial robustness of demographic fairness in face attribute recognitionProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/59(527-535)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/59
Bhaumik KWoo S(2023)Exploiting Inconsistencies in Object Representations for Deepfake Video DetectionProceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes10.1145/3595353.3595885(11-15)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3595353.3595885
Le BTariq SAbuadbba AMoore KWoo S(2023)Why Do Facial Deepfake Detectors Fail?Proceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes10.1145/3595353.3595882(24-28)Online publication date: 10-Jul-2023
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Cho BLe BKim JWoo STariq SAbuadbba AMoore KFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Towards Understanding of Deepfake Videos in the WildProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614729(4530-4537)Online publication date: 21-Oct-2023
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Yin QLu WLi BHuang J(2023)Dynamic Difference Learning With Spatio–Temporal Correlation for Deepfake Video DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329075218(4046-4058)Online publication date: 1-Jan-2023
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