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A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method.
A morphable model for the synthesis of 3D faces
Volker Blanz and Thomas Vetter · 1999
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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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 · 2012
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Deep metric learning using triplet network
Elad Hoffer and Nir Ailon · 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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Face2face: Real-time face capture and reenactment of rgb videos
Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt, and Matthias Nießner · 2016
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Localizing and orienting street views using overhead imagery
Nam N Vo and James Hays · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 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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Voxceleb2: Deep speaker recognition
Joon Son Chung, Arsha Nagrani, and Andrew Zisserman · 2018
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ForensicTransfer: Weakly-supervised domain adaptation for forgery detection
Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva · 2018
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Deepfake video detection using recurrent neural networks
David Güera and Edward J Delp · 2018
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Fighting fake news: Image splice detection via learned self-consistency
Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros · 2018
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In Ictu Oculi: Exposing AI created fake videos by detecting eye blinking
Yuezun Li, Ming-Ching Chang, and Siwei Lyu · 2018
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Self-supervised learning of a facial attribute embedding from video
Olivia Wiles, A. Sophia Koepke, and Andrew Zisserman · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Protecting world leaders against deep fakes
Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li · 2019
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Extracting camera-based fingerprints for video forensics
Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva · 2019
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The deepfake detection challenge (DFDC) preview dataset
Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram, and Cristian Canton Ferrer · 2019
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Predicting Heart Rate Variations of Deepfake Videos using Neural ODE
Steven Fernandes, Sunny Raj, Eddy Ortiz, Iustina Vintila, Margaret Salter, Gordana Urosevic, and Sumit Jha · 2019
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Exposing deepfake videos by detecting face warping artifacts
Yuezun Li and Siwei Lyu · 2019
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DeepFakes detection Dataset, 2019
Noiseprint: A CNN-Based Camera Model Fingerprint
Davide Cozzolino and Luisa Verdoliva · 2020
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On the detection of digital face manipulation
Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, and Anil K Jain · 2020
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The deepfake detection challenge dataset
Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer · 2020
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Towards generalizable deepfake detection with locality-aware autoencoder
Mengnan Du, Shiva K. Pentyala, Yuening Li, and Xia Hu · 2020
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Towards fast, accurate and stable 3d dense face alignment
Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li · 2020
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Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection
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N. Dufour, A. Gully, P. Karlsson, A.V. Vorbyov, T. Leung, J. Childs and C. Bregler · 2019
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Faceforensics++: Learning to detect manipulated facial images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
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First order motion model for image animation
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Deferred neural rendering: Image synthesis using neural textures
Justus Thies, Michael Zollhöfer, and Matthias Nießner · 2019
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Multi-similarity loss with general pair weighting for deep metric learning
Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, and Matthew R Scott · 2019
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Exposing deep fakes using inconsistent head poses
Xin Yang, Yuezun Li, and Siwei Lyu · 2019
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Liming Jiang, Ren Li, Wayne Wu, Chen Qian, and Chen Change Loy · 2020
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Detecting deepfakes with metric learning
Akash Kumar, Arnav Bhavsar, and Rajesh Verma · 2020
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Face x-ray for more general face forgery detection
Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo · 2020
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Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
Yuezun Li, Pu Sun, Honggang Qi, and Siwei Lyu · 2020
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Two-branch recurrent networkfor isolating deepfakes in videos
Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed · 2020
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DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, and Jianjun Zhao · 2020
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Deepfakes and beyond: A survey of face manipulation and fake detection
R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia · 2020
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Media forensics and deepfakes: an overview
Luisa Verdoliva · 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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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Videoforensicshq: Detecting high-quality manipulated face videos
Gereon Fox, Wentao Liu, Hyeongwoo Kim, Hans-Peter Seidel, Mohamed Elgharib, and Christian Theobalt · 2021
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