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Deepfake detection methods based on convolutional neural networks (CNN) have demonstrated high accuracy.
S. J. Nowlan and G. E. Hinton, “Simplifying neural networks by soft weight-sharing,”
1992
Earlier work this paper cites.
J. Lukás, J. J. Fridrich, and M. Goljan, “Detecting digital image forgeries using sensor pattern noise,” in
2006
Earlier work this paper cites.
G. Chierchia, S. Parrilli, G. Poggi, L. Verdoliva, and C. Sansone, “Prnu-based detection of small-size image forgeries,”
2011
Earlier work this paper cites.
J. J. Fridrich and J. Kodovský, “Rich models for steganalysis of digital images,”
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,”
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
FaceSwap, “Faceswap github,”
2016
Earlier work this paper cites.
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,”
2016
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in
2016
Earlier work this paper cites.
J. Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2face: Real-time face capture and reenactment of RGB videos,” in
2016
Earlier work this paper cites.
DeepFakes, “Deepfakes github,”
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in
2017
Earlier work this paper cites.
G. Kang, X. Dong, L. Zheng, and Y. Yang, “Patchshuffle regularization,”
2017
Earlier work this paper cites.
X. Shen, X. Tian, S. Sun, and D. Tao, “Patch reordering: A novelway to achieve rotation and translation invariance in convolutional neural networks,” in
2017
Earlier work this paper cites.
P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, “Two-stream neural networks for tampered face detection,”
2017
Earlier work this paper cites.
S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” in
2017
Earlier work this paper cites.
S. Ravanbakhsh, J. G. Schneider, and B. Póczos, “Deep learning with sets and point clouds,”
2017
Earlier work this paper cites.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,”
2017
Earlier work this paper cites.
Y. Choi, M.-J. Choi, M. S. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,”
2018
Earlier work this paper cites.
A. Pumarola, A. Agudo, A. M. Martinez, A. Sanfeliu, and F. Moreno-Noguer, “Ganimation: Anatomically-aware facial animation from a single image,”
2018
Cited alongside, same era.
C. Chen, S. McCloskey, and J. Yu, “Focus manipulation detection via photometric histogram analysis,” in
2018
Cited alongside, same era.
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, “Mesonet: a compact facial video forgery detection network,”
2018
Cited alongside, same era.
Y. Nirkin, Y. Keller, and T. Hassner, “Fsgan: Subject agnostic face swapping and reenactment,”
2019
Cited alongside, same era.
2019
Cited alongside, same era.
V. Bazarevsky, Y. Kartynnik, A. Vakunov, K. Raveendran, and M. Grundmann, “Blazeface: Sub-millisecond neural face detection on mobile gpus,”
2019
Later among the works it cites.
M. Du, S. K. Pentyala, Y. Li, and X. Hu, “Towards generalizable deepfake detection with locality-aware autoencoder,” in
2020
Later among the works it cites.
Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-df: A large-scale challenging dataset for deepfake forensics,” in
2020
Later among the works it cites.
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, “Face x-ray for more general face forgery detection,” in
2020
Later among the works it cites.
Y. Qian, G. Yin, L. Sheng, Z. Chen, and J. Shao, “Thinking in frequency: Face forgery detection by mining frequency-aware clues,” in
2020
Later among the works it cites.
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T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,”
2019
Cited alongside, same era.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in
2019
Cited alongside, same era.
H. H. Nguyen, J. Yamagishi, and I. Echizen, “Capsule-forensics: Using capsule networks to detect forged images and videos,” in
2019
Cited alongside, same era.
Y. Li and S. Lyu, “Exposing deepfake videos by detecting face warping artifacts,”
2019
Cited alongside, same era.
R. Durall, M. Keuper, F.-J. Pfreundt, and J. Keuper, “Unmasking deepfakes with simple features,”
2019
Cited alongside, same era.
M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,”
2019
Cited alongside, same era.
Nvidia, “This person does not exist,”
2019
Cited alongside, same era.
Faceswap-GAN, “Faceswap-gan github,”
2020
Later among the works it cites.
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, “Random erasing data augmentation,”
2020
Later among the works it cites.
M. Aprilpyone and H. Kiya, “Encryption inspired adversarial defense for visual classification,”
2020
Later among the works it cites.
L. Jiang, W. Wu, R. Li, C. Qian, and C. C. Loy, “Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection,”
2020
Later among the works it cites.
I. Masi, A. Killekar, R. M. Mascarenhas, S. P. Gurudatt, and W. AbdAlmageed, “Two-branch recurrent network for isolating deepfakes in videos,” in
2020
Later among the works it cites.
Sensity, “The state of deepfakes 2020: Updates on statistics and trends,”
2021
Later among the works it cites.
H. Zhao, W. Zhou, D. Chen, T. Wei, W. Zhang, and N. Yu, “Multi-attentional deepfake detection,”
2021
Later among the works it cites.
Y. Luo, Y. Zhang, J. Yan, and W. Liu, “Generalizing face forgery detection with high-frequency features,”
2021
Later among the works it cites.
M. Hong, S. Li, Y. Yang, F. Zhu, Q. Zhao, and L. Lu, “Sspnet: Scale selection pyramid network for tiny person detection from uav images,”
2021
Later among the works it cites.
C. Wang and W. Deng, “Representative forgery mining for fake face detection,”
2021
Later among the works it cites.
2021
Later among the works it cites.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,”
2021
Later among the works it cites.
M. S. Rana, M. N. Nobi, B. Murali, and A. H. Sung, “Deepfake detection: A systematic literature review,”
2022
Closest in time.
L. Xie, X. Chen, K. Bi, L. Wei, Y. Xu, Z. Chen, L. Wang, A. Xiao, J. Chang, X. Zhang, and Q. Tian, “Weight-sharing neural architecture search: A battle to shrink the optimization gap,”
2022
Closest in time.
P. Yu, J. Fei, Z. Xia, Z. Zhou, and J. Weng, “Improving generalization by commonality learning in face forgery detection,”
2022
Closest in time.
L. Chen, Y. Zhang, Y. Song, L. Liu, and J. Wang, “Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection,”
2022
Closest in time.