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Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Face2face: Real-time face capture and reenactment of rgb videos
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner · 2016
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
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[EB/OL], 2018
Fakeapp · 2018
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https://github.com/deepfakes/faceswap
Github deepfake faceswap · 2018
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Mesonet: a compact facial video forgery detection network
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen · 2018
Earlier work this paper cites.
Forensictransfer: Weakly-supervised domain adaptation for forgery detection
D. Cozzolino, J. Thies, A. Rössler, C. Riess, M. Nießner, and L. Verdoliva · 2018
Earlier work this paper cites.
Deepfakes: a new threat to face recognition? assessment and detection
P. Korshunov and S. Marcel · 2018
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Learning to generalize: Meta-learning for domain generalization
D. Li, Y. Yang, Y.-Z. Song, and T. Hospedales · 2018
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In ictu oculi: Exposing ai created fake videos by detecting eye blinking
Y. Li, M.-C. Chang, and S. Lyu · 2018
Earlier work this paper cites.
Exposing deepfake videos by detecting face warping artifacts
Y. Li and S. Lyu · 2018
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[EB/OL], 2019
Deepfakes faceswap · 2019
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Parameter-efficient transfer learning for nlp
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
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Multi-task learning for detecting and segmenting manipulated facial images and videos
H. H. Nguyen, F. Fang, J. Yamagishi, and I. Echizen · 2019
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Faceforensics++: Learning to detect manipulated facial images
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. Le · 2019
Cited alongside, same era.
Deferred neural rendering: Image synthesis using neural textures
J. Thies, M. Zollhöfer, and M. Nießner · 2019
Cited alongside, same era.
Exposing deep fakes using inconsistent head poses
X. Yang, Y. Li, and S. Lyu · 2019
Cited alongside, same era.
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html
Deepfakedetection · 2020
Cited alongside, same era.
Drl-fas: A novel framework based on deep reinforcement learning for face anti-spoofing
R. Cai, H. Li, S. Wang, C. Chen, and A. C. Kot · 2020
Cited alongside, same era.
On the detection of digital face manipulation
H. Dang, F. Liu, J. Stehouwer, X. Liu, and A. K. Jain · 2020
Cited alongside, same era.
Generalizing face forgery detection with high-frequency features
Y. Luo, Y. Zhang, J. Yan, and W. Liu · 2021
Later among the works it cites.
Domain general face forgery detection by learning to weight
K. Sun, H. Liu, Q. Ye, Y. Gao, J. Liu, L. Shao, and R. Ji · 2021
Later among the works it cites.
Joint audio-visual deepfake detection
Y. Zhou and S.-N. Lim · 2021
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Learning meta pattern for face anti-spoofing
R. Cai, Z. Li, R. Wan, H. Li, Y. Hu, and A. C. Kot · 2022
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Leveraging real talking faces via self-supervision for robust forgery detection
A. Haliassos, R. Mira, S. Petridis, and M. Pantic · 2022
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Visual prompt tuning
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim · 2022
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The deepfake detection challenge dataset
B. Dolhansky, J. Bitton, B. Pflaum, J. Lu, R. Howes, M. Wang, and C. Canton Ferrer · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
Cited alongside, same era.
Face x-ray for more general face forgery detection
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo · 2020
Cited alongside, same era.
Celeb-df: A large-scale challenging dataset for deepfake forensics
Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu · 2020
Cited alongside, same era.
Two-branch recurrent network for isolating deepfakes in videos
I. Masi, A. Killekar, R. M. Mascarenhas, S. P. Gurudatt, and W. AbdAlmageed · 2020
Cited alongside, same era.
Thinking in frequency: Face forgery detection by mining frequency-aware clues
Y. Qian, G. Yin, L. Sheng, Z. Chen, and J. Shao · 2020
Cited alongside, same era.
Detect and locate: Exposing face manipulation by semantic-and noise-level telltales
C. Kong, B. Chen, H. Li, S. Wang, A. Rocha, and S. Kwong · 2022
Later among the works it cites.
Digital and physical face attacks: Reviewing and one step further
C. Kong, S. Wang, and H. Li · 2022
Later among the works it cites.
Beyond the pixel world: A novel acoustic-based face anti-spoofing system for smartphones
C. Kong, K. Zheng, S. Wang, A. Rocha, and H. Li · 2022
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One-class knowledge distillation for face presentation attack detection
Z. Li, R. Cai, H. Li, K.-Y. Lam, Y. Hu, and A. C. Kot · 2022
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Detecting deepfakes with self-blended images
K. Shiohara and T. Yamasaki · 2022
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Towards robust rain removal against adversarial attacks: A comprehensive benchmark analysis and beyond
Y. Yu, W. Yang, Y.-P. Tan, and A. C. Kot · 2022
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M3fas: An accurate and robust multimodal mobile face anti-spoofing system
C. Kong, K. Zheng, Y. Liu, S. Wang, A. Rocha, and H. Li · 2023
Closest in time.
Backdoor attacks against deep image compression via adaptive frequency trigger
Y. Yu, Y. Wang, W. Yang, S. Lu, Y.-p. Tan, and A. C. Kot · 2023
Closest in time.