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In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as {\em DeepFake} videos hereafter) from real videos.
Exposing digital forgeries by detecting traces of resampling
Alin C Popescu and Hany Farid · 2005
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On resampling detection and its application to detect image tampering
S Prasad and KR Ramakrishnan · 2006
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Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue
Matthias Kirchner · 2008
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Hiding traces of resampling in digital images
Matthias Kirchner and Rainer Bohme · 2008
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Babak Mahdian and Stanislav Saic · 2008
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Dlib-ml: A machine learning toolkit
Davis E. King · 2009
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On resampling detection in re-compressed images
Matthias Kirchner and Thomas Gloe · 2009
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Multi-region probabilistic histograms for robust and scalable identity inference
Conrad Sanderson and Brian C Lovell · 2009
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On the role of differentiation for resampling detection
Nahuel Dalgaard, Carlos Mosquera, and Fernando Pérez-González · 2010
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Robust resampling detection in digital images
Hieu Cuong Nguyen and Stefan Katzenbeisser · 2012
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Image forensics with rotation-tolerant resampling detection
Ruohan Qian, Weihai Li, Nenghai Yu, and Zhuo Hao · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Image resampling detection based on texture classification
Xiaodan Hou, Tao Zhang, Gang Xiong, Yan Zhang, and Xin Ping · 2014
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Karen Simonyan and Andrew Zisserman · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
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Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Aayush Bansal, Shugao Ma, Deva Ramanan, and Yaser Sheikh · 2018
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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
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Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Niessner · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Detection and localization of image forgeries using resampling features and deep learning
Jason Bunk, Jawadul H Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, BS Manjunath, Shivkumar Chandrasekaran, Amit K Roy-Chowdhury, and Lawrence Peterson · 2017
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Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Unsupervised image-to-image translation networks
Ming-Yu Liu, Thomas Breuel, and Jan Kautz · 2017
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2017
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Davide Cozzolino 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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Deepfakes: a new threat to face recognition? assessment and detection
Pavel Korshunov and Sébastien Marcel · 2018
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Detection of deep network generated images using disparities in color components
Haodong Li, Bin Li, Shunquan Tan, and Jiwu Huang · 2018
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In ictu oculi: Exposing ai generated fake face videos by detecting eye blinking
Yuezun Li, Ming-Ching Chang, and Siwei Lyu · 2018
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Detecting gan-generated imagery using color cues
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Exposing deep fakes using inconsistent head poses
Xin Yang, Yuezun Li, and Siwei Lyu · 2019
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