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1M-Deepfakes Detection Challenge

Authors:

Dimitrios Kollias,

Kalin Stefanov,

Usman TariqAuthors Info & Claims

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

Pages 11355 - 11359

https://doi.org/10.1145/3664647.3689145

Published: 28 October 2024 Publication History

Abstract

The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M.

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). 1--7. ISSN: 2157--4774.

[2]

John Brandon. 2019. There Are Now 15,000 Deepfake Videos on Social Media. Yes, You Should Worry. Forbes (Oct. 2019).

[3]

Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. 2015. ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 961--970.

[4]

Zhixi Cai, Shreya Ghosh, Aman Pankaj Adatia, Munawar Hayat, Abhinav Dhall, and Kalin Stefanov. 2023. AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset. arXiv:2311.15308 [cs].

[5]

Zhixi Cai, Shreya Ghosh, Abhinav Dhall, Tom Gedeon, Kalin Stefanov, and Munawar Hayat. 2023. Glitch in the matrix: A large scale benchmark for content driven audio--visual forgery detection and localization. Computer Vision and Image Understanding, Vol. 236 (Nov. 2023), 103818.

Digital Library

[6]

Zhixi Cai, Shreya Ghosh, Kalin Stefanov, Abhinav Dhall, Jianfei Cai, Hamid Rezatofighi, Reza Haffari, and Munawar Hayat. 2023. MARLIN: Masked Autoencoder for Facial Video Representation LearnINg. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, Vancouver, BC, Canada, 1493--1504.

[7]

Zhixi Cai, Kalin Stefanov, Abhinav Dhall, and Munawar Hayat. 2022. Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Sydney, Australia, 1--10.

[8]

Francois Chollet. 2017. Xception: Deep Learning With Depthwise Separable Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1251--1258.

[9]

Komal Chugh, Parul Gupta, Abhinav Dhall, and Ramanathan Subramanian. 2020. Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization. In Proceedings of the 28th ACM International Conference on Multimedia (MM '20). Association for Computing Machinery, New York, NY, USA, 439--447.

Digital Library

[10]

Davide Alessandro Coccomini, Nicola Messina, Claudio Gennaro, and Fabrizio Falchi. 2022. Combining EfficientNet and Vision Transformers for Video Deepfake Detection. In Image Analysis and Processing -- ICIAP 2022 (Lecture Notes in Computer Science), Stan Sclaroff, Cosimo Distante, Marco Leo, Giovanni M. Farinella, and Federico Tombari (Eds.). Springer International Publishing, Cham, 219--229.

[11]

Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. 2020. The DeepFake Detection Challenge (DFDC) Dataset. arXiv: 2006.07397 [cs].

[12]

Chao Feng, Ziyang Chen, and Andrew Owens. 2023. Self-Supervised Video Forensics by Audio-Visual Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10491--10503.

[13]

Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, and Devi Parikh. 2022. Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer. In Proceedings of the European Conference on Computer Vision (ECCV) (Lecture Notes in Computer Science), Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer Nature Switzerland, Cham, 102--118.

[14]

Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. 2021. Lips Don't Lie: A Generalisable and Robust Approach To Face Forgery Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5039--5049.

[15]

Yinan He, Bei Gan, Siyu Chen, Yichun Zhou, Guojun Yin, Luchuan Song, Lu Sheng, Jing Shao, and Ziwei Liu. 2021. ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4360--4369.

[16]

Hafsa Ilyas, Ali Javed, and Khalid Mahmood Malik. 2023. AVFakeNet: A unified end-to-end Dense Swin Transformer deep learning model for audio--visual deepfakes detection. Applied Soft Computing, Vol. 136 (March 2023), 110124.

[17]

Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy. 2020. DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2889--2898.

[18]

Ziyue Jiang, Jinglin Liu, Yi Ren, Jinzheng He, Chen Zhang, Zhenhui Ye, Pengfei Wei, Chunfeng Wang, Xiang Yin, Zejun Ma, and Zhou Zhao. 2023. Mega-TTS 2: Zero-Shot Text-to-Speech with Arbitrary Length Speech Prompts. arXiv:2307.07218 [cs, eess].

[19]

Ziyue Jiang, Yi Ren, Zhenhui Ye, Jinglin Liu, Chen Zhang, Qian Yang, Shengpeng Ji, Rongjie Huang, Chunfeng Wang, Xiang Yin, Zejun Ma, and Zhou Zhao. 2023. Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias. arXiv:2306.03509 [cs, eess].

[20]

Hasam Khalid, Shahroz Tariq, and Simon S. Woo. 2021. FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset. arXiv: 2108.05080 [cs].

[21]

Muhammad Riyyan Khan, Shahzeb Naeem, Usman Tariq, Abhinav Dhall, Malik Nasir Afzal Khan, Fares Al Shargie, and Hasan Al-Nashash. 2023. Exploring Neurophysiological Responses to Cross-Cultural Deepfake Videos. In Companion Publication of the 25th International Conference on Multimodal Interaction. 41--45.

Digital Library

[22]

Pavel Korshunov and Sebastien Marcel. 2018. DeepFakes: a New Threat to Face Recognition? Assessment and Detection. arXiv:1812.08685 [cs].

[23]

Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, and Gyeongsu Chae. 2021. KoDF: A Large-Scale Korean DeepFake Detection Dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 10744--10753.

[24]

Trung-Nghia Le, Huy H. Nguyen, Junichi Yamagishi, and Isao Echizen. 2021. OpenForensics: Large-Scale Challenging Dataset for Multi-Face Forgery Detection and Segmentation In-the-Wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 10117--10127.

[25]

Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. 2020. Face X-Ray for More General Face Forgery Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5001--5010.

[26]

Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. 2020. Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3207--3216.

[27]

Xuechen Liu, Xin Wang, Md Sahidullah, Jose Patino, Héctor Delgado, Tomi Kinnunen, Massimiliano Todisco, Junichi Yamagishi, Nicholas Evans, Andreas Nautsch, and Kong Aik Lee. 2023. ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31 (2023), 2507--2522.

Digital Library

[28]

Shahzeb Naeem, Muhammad Riyyan Khan, Usman Tariq, Abhinav Dhall, Carlos Ivan Colon, and Hasan Al-Nashash. 2024. Generation and Detection of Sign Language Deepfakes-A Linguistic and Visual Analysis. arXiv preprint arXiv:2404.01438 (2024).

[29]

Kartik Narayan, Harsh Agarwal, Kartik Thakral, Surbhi Mittal, Mayank Vatsa, and Richa Singh. 2023. DF-Platter: Multi-Face Heterogeneous Deepfake Dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9739--9748.

[30]

Dufou Nick and Jigsaw Andrew. 2019. Contributing Data to Deepfake Detection Research.

[31]

Trevine Oorloff, Surya Koppisetti, Nicolò Bonettini, Divyaraj Solanki, Ben Colman, Yaser Yacoob, Ali Shahriyari, and Gaurav Bharaj. 2024. AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 27102--27112.

[32]

Alexis Plaquet and Hervé Bredin. 2023. Powerset multi-class cross entropy loss for neural speaker diarization. In INTERSPEECH 2023. ISCA, 3222--3226.

[33]

Diego Pérez-Vieites, Juan José Moreira-Pérez, Ángel Aragón-Kifute, Raquel Román-Sarmiento, and Rubén Castro-González. 2024. Vigo: Audiovisual Fake Detection and Segment Localization. In ACM international conference on multimedia.

[34]

Muhammad Anas Raza and Khalid Mahmood Malik. 2023. Multimodaltrace: Deepfake Detection Using Audiovisual Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 993--1000.

[35]

Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Niessner. 2019. FaceForensics: Learning to Detect Manipulated Facial Images. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1--11.

[36]

Ian Sample. 2020. What are deepfakes -- and how can you spot them? The Guardian (Jan. 2020).

[37]

Conrad Sanderson (Ed.). 2002. The VidTIMIT Database. IDIAP.

[38]

Oscar Schwartz. 2018. You thought fake news was bad? Deep fakes are where truth goes to die. The Guardian (Nov. 2018).

[39]

Rui Shao, Tianxing Wu, Jianlong Wu, Liqiang Nie, and Ziwei Liu. 2024. Detecting and Grounding Multi-Modal Media Manipulation and Beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024), 1--18.

Digital Library

[40]

Kai Shen, Zeqian Ju, Xu Tan, Yanqing Liu, Yichong Leng, Lei He, Tao Qin, Sheng Zhao, and Jiang Bian. 2023. NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers. arXiv:2304.09116 [cs, eess].

[41]

Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, and Dacheng Tao. 2023. TriDet: Temporal Action Detection With Relative Boundary Modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18857--18866.

[42]

Kaede Shiohara and Toshihiko Yamasaki. 2022. Detecting Deepfakes With Self-Blended Images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18720--18729.

[43]

Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. 2022. Make-A-Video: Text-to-Video Generation without Text-Video Data. arXiv:2209.14792 [cs].

[44]

Daniel Thomas. 2020. Deepfakes: A threat to democracy or just a bit of fun? BBC News (Jan. 2020).

[45]

Junke Wang, Zuxuan Wu, Wenhao Ouyang, Xintong Han, Jingjing Chen, Yu-Gang Jiang, and Ser-Nam Li. 2022. M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection. In Proceedings of the 2022 International Conference on Multimedia Retrieval (ICMR '22). Association for Computing Machinery, New York, NY, USA, 615--623.

Digital Library

[46]

Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, and Yu Qiao. 2023. VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14549--14560.

[47]

Yi Wang, Kunchang Li, Yizhuo Li, Yinan He, Bingkun Huang, Zhiyu Zhao, Hongjie Zhang, Jilan Xu, Yi Liu, Zun Wang, Sen Xing, Guo Chen, Junting Pan, Jiashuo Yu, Yali Wang, Limin Wang, and Yu Qiao. 2022. InternVideo: General Video Foundation Models via Generative and Discriminative Learning. arXiv:2212.03191 [cs].

[48]

Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. 2023. Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7623--7633.

[49]

Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. 2023. Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1--5. ISSN: 2379--190X.

[50]

Zhen Xu, Sergio Escalera, Adrien Pavao, Magali Richard, Wei-Wei Tu, Quanming Yao, Huan Zhao, and Isabelle Guyon. 2022. Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns, Vol. 3, 7 (2022).

[51]

Ke Yang, Peng Qiao, Dongsheng Li, Shaohe Lv, and Yong Dou. 2018. Exploring Temporal Preservation Networks for Precise Temporal Action Localization. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, 1 (April 2018).

[52]

Wenyuan Yang, Xiaoyu Zhou, Zhikai Chen, Bofei Guo, Zhongjie Ba, Zhihua Xia, Xiaochun Cao, and Kui Ren. 2023. AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake. IEEE Transactions on Information Forensics and Security, Vol. 18 (2023), 2015--2029.

Digital Library

[53]

Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li, Zheng Lian, and Bin Liu. 2022. ADD 2022: the First Audio Deep Synthesis Detection Challenge. arXiv:2202.08433 [cs, eess].

[54]

Yang Yu, Xiaolong Liu, Rongrong Ni, Siyuan Yang, Yao Zhao, and Alex C. Kot. 2023. PVASS-MDD: Predictive Visual-audio Alignment Self-supervision for Multimodal Deepfake Detection. IEEE Transactions on Circuits and Systems for Video Technology (2023), 1--1.

[55]

Chen-Lin Zhang, Jianxin Wu, and Yin Li. 2022. ActionFormer: Localizing Moments of Actions with Transformers. In Proceedings of the European Conference on Computer Vision (ECCV) (Lecture Notes in Computer Science), Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer Nature Switzerland, Cham, 492--510.

[56]

Hang Zhang, Xin Li, and Lidong Bing. 2023. Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding. arXiv:2306.02858 [cs, eess].

[57]

Rui Zhang, Hongxia Wang, Mingshan Du, Hanqing Liu, Yang Zhou, and Qiang Zeng. 2023. UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization. In Proceedings of the 31st ACM International Conference on Multimedia (MM '23). Association for Computing Machinery, New York, NY, USA, 8749--8759.

Digital Library

[58]

Tianfei Zhou, Wenguan Wang, Zhiyuan Liang, and Jianbing Shen. 2021. Face Forensics in the Wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5778--5788.

[59]

Bojia Zi, Minghao Chang, Jingjing Chen, Xingjun Ma, and Yu-Gang Jiang. 2020. WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection. In Proceedings of the 28th ACM International Conference on Multimedia (MM '20). Association for Computing Machinery, New York, NY, USA, 2382--2390.

Digital Library

Cited By

Ghosh SCai ZDhall AKollias DGoecke RGedeon TTao JGhosh SLian ZCai ZSchuller BDhall AZhao GKollias DCambria EGoecke RGedeon T(2024)MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective ComputingProceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing10.1145/3689092.3690042(1-6)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689092.3690042
Wang YWu XZhang JJing MLu KYu JSu WGao FLiu QSun JLiang JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Building Robust Video-Level Deepfake Detection via Audio-Visual Local-Global InteractionsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688985(11370-11376)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3688985
Zhang YMiao CLuo MLi JDeng WYao WLi ZHu BFeng WGong TChu QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MFMS: Learning Modality-Fused and Modality-Specific Features for Deepfake Detection and Localization TasksProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688984(11365-11369)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3688984
Show More Cited By

Index Terms

1M-Deepfakes Detection Challenge
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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Published In

cover image ACM Conferences

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

October 2024

11719 pages

ISBN:9798400706868

DOI:10.1145/3664647

General Chairs:
Jianfei Cai
Monash University, Australia
,
Mohan Kankanhalli
NUS, Singapore
,
Balakrishnan Prabhakaran
UT Dallas, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Program Chairs:
Ramanathan Subramanian
University of Canberra & IIT Ropar, Australia
,
Liang Zheng
Australian National University, Australia
,
Vivek K. Singh
Rutgers University, USA
,
Pablo Cesar
Centrum Wiskunde & Informatica, Netherlands
,
Lexing Xie
Australian National University, Australia
,
Dong Xu
University of Hong Kong, Hong Kong

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 28 October 2024

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MM '24

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SIGMM

MM '24: The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne VIC, Australia

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MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;

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

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Cited By

Ghosh SCai ZDhall AKollias DGoecke RGedeon TTao JGhosh SLian ZCai ZSchuller BDhall AZhao GKollias DCambria EGoecke RGedeon T(2024)MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective ComputingProceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing10.1145/3689092.3690042(1-6)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689092.3690042
Wang YWu XZhang JJing MLu KYu JSu WGao FLiu QSun JLiang JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Building Robust Video-Level Deepfake Detection via Audio-Visual Local-Global InteractionsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688985(11370-11376)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3688985
Zhang YMiao CLuo MLi JDeng WYao WLi ZHu BFeng WGong TChu QCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MFMS: Learning Modality-Fused and Modality-Specific Features for Deepfake Detection and Localization TasksProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688984(11365-11369)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3688984
Pérez-Vieites DMoreira-Pérez JAragón-Kifute ÁRomán-Sarmiento RCastro-González RCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Vigo: Audiovisual Fake Detection and Segment LocalizationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688983(11360-11364)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3688983

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