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There have been emerging a number of benchmarks and techniques for the detection of deepfakes.
Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Multi-region probabilistic histograms for robust and scalable identity inference
C. Sanderson and B.C. Lovell · 2009
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Face2face: Real-time face capture and reenactment of rgb videos
Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner · 2016
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Photographic image synthesis with cascaded refinement networks
Qifeng Chen and Vladlen Koltun · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Overcoming catastrophic forgetting by incremental moment matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Two-stream neural networks for tampered face detection
Peng Zhou, Xintong Han, Vlad I Morariu, and Larry S Davis · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Mesonet: a compact facial video forgery detection network
Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen · 2018
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Efficient lifelong learning with a-gem
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2018
Cited alongside, same era.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
Cited alongside, same era.
Overcoming catastrophic forgetting for continual learning via model adaptation
Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, and Rui Yan · 2018
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
Cited alongside, same era.
Exposing deep fakes using inconsistent head poses
Xin Yang, Yuezun Li, and Siwei Lyu · 2019
Later among the works it cites.
Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
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The deepfake detection challenge (dfdc) dataset
Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer · 2020
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Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
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Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection
Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy · 2020
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Celeb-df: A large-scale challenging dataset for deepfake forensics
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Deepfakes: a new threat to face recognition? assessment and detection
Pavel Korshunov and Sébastien Marcel · 2018
Cited alongside, same era.
In ictu oculi: Exposing ai created fake videos by detecting eye blinking
Yuezun Li, Ming-Ching Chang, and Siwei Lyu · 2018
Cited alongside, same era.
Exposing deepfake videos by detecting face warping artifacts
Yuezun Li and Siwei Lyu · 2018
Cited alongside, same era.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Cited alongside, same era.
Protecting world leaders against deep fakes
Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li · 2019
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu · 2020
Later among the works it cites.
Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
Later among the works it cites.
Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2020
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2020
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Topology-preserving class-incremental learning
Xiaoyu Tao, Xinyuan Chang, Xiaopeng Hong, Xing Wei, and Yihong Gong · 2020
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CNN-generated images are surprisingly easy to spot… for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
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Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer · 2020
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Wilddeepfake: A challenging real-world dataset for deepfake detection
Bojia Zi, Minghao Chang, Jingjing Chen, Xingjun Ma, and Yu-Gang Jiang · 2020
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Convit: Improving vision transformers with soft convolutional inductive biases
Stéphane d’Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun · 2021
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Towards incremental transformers: An empirical analysis of transformer models for incremental nlu
Patrick Kahardipraja, Brielen Madureira, and David Schlangen · 2021
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Cored: Generalizing fake media detection with continual representation using distillation
Minha Kim, Shahroz Tariq, and Simon S Woo · 2021
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Essentials for class incremental learning
Sudhanshu Mittal, Silvio Galesso, and Thomas Brox · 2021
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Efficient conditional gan transfer with knowledge propagation across classes
Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, and Luc Van Gool · 2021
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Layerwise optimization by gradient decomposition for continual learning
Shixiang Tang, Dapeng Chen, Jinguo Zhu, Shijie Yu, and Wanli Ouyang · 2021
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Class-incremental learning with generative classifiers
Gido M van de Ven, Zhe Li, and Andreas S Tolias · 2021
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Training networks in null space of feature covariance for continual learning
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu · 2021
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Improving vision transformers for incremental learning
Pei Yu, Yinpeng Chen, Ying Jin, and Zicheng Liu · 2021
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Incremental prototype prompt-tuning with pre-trained representation for class incremental learning
Jieren Deng, Jianhua Hu, Haojian Zhang, and Yunkuan Wang · 2022
Closest in time.
Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
Closest in time.
Detecting real-time deep-fake videos using active illumination
Candice R Gerstner and Hany Farid · 2022
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Technical report for iccv 2021 challenge sslad-track3b: Transformers are better continual learners
Duo Li, Guimei Cao, Yunlu Xu, Zhanzhan Cheng, and Yi Niu · 2022
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Deepfake disrupter: The detector of deepfake is my friend
Xueyu Wang, Jiajun Huang, Siqi Ma, Surya Nepal, and Chang Xu · 2022
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Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
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