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Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals.
Transferable adversarial perturbations
Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J.T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Earlier work this paper cites.
Joint face detection and alignment using multitask cascaded convolutional networks
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Earlier work this paper cites.
Fast feature fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri, Utsav Garg, and R Venkatesh Babu · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Distinguishing computer graphics from natural images using convolution neural networks
Nicolas Rahmouni, Vincent Nozick, Junichi Yamagishi, and Isao Echizen · 2017
Cited alongside, same era.
MesoNet: a compact facial video forgery detection network
Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen · 2018
Cited alongside, same era.
Universal adversarial perturbation via prior driven uncertainty approximation
Hong Liu, Rongrong Ji, Jie Li, Baochang Zhang, Yue Gao, Yongjian Wu, and Feiyue Huang · 2019
Later among the works it cites.
Universal adversarial perturbations for speech recognition systems
Paarth Neekhara, Shehzeen Hussain, Prakhar Pandey, Shlomo Dubnov, Julian McAuley, and Farinaz Koushanfar · 2019
Later among the works it cites.
FSGAN: Subject agnostic face swapping and reenactment
Yuval Nirkin, Yosi Keller, and Tal Hassner · 2019
Later among the works it cites.
FaceForensics++: Learning to detect manipulated facial images
Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Niessner · 2019
Later among the works it cites.
Curls & whey: Boosting black-box adversarial attacks
Yucheng Shi, Siyu Wang, and Yahong Han · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
Cited alongside, same era.
Ask, acquire, and attack: Data-free uap generation using class impressions
Konda Reddy Mopuri, Phani Krishna Uppala, and R Venkatesh Babu · 2018
Cited alongside, same era.
Physical adversarial examples for object detectors
Dawn Song, Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramèr, Atul Prakash, and Tadayoshi Kohno · 2018
Cited alongside, same era.
Deepfake video detection through optical flow based CNN
Irene Amerini, Leonardo Galteri, Roberto Caldelli, and Alberto Del Bimbo · 2019
Cited alongside, same era.
Universal adversarial attacks on text classifiers
Melika Behjati, Seyed-Mohsen Moosavi-Dezfooli, Mahdieh Soleymani Baghshah, and Pascal Frossard · 2019
Cited alongside, same era.
Improving black-box adversarial attacks with a transfer-based prior
Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
Cited alongside, same era.
The DeepFake Detection Challenge (DFDC) preview dataset
Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram, and Cristian Canton Ferrer · 2019
Cited alongside, same era.
Mingxing Tan and Quoc V Le · 2019
Later among the works it cites.
The emergence of deepfake technology: A review
Mika Westerlund · 2019
Later among the works it cites.
Few-shot adversarial learning of realistic neural talking head models
Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, and Victor Lempitsky · 2019
Later among the works it cites.
Evading deepfake-image detectors with white- and black-box attacks
Nicholas Carlini and Hany Farid · 2020
Closest in time.
The DeepFake Detection Challenge (DFDC) dataset
Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer · 2020
Closest in time.
Adversarial perturbations fool deepfake detectors
Apurva Gandhi and Shomik Jain · 2020
Closest in time.
Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples
Paarth Neekhara, Shehzeen Hussain, Malhar Jere, Farinaz Koushanfar, and Julian McAuley · 2020
Closest in time.
Media forensics and deepfakes: an overview
Luisa Verdoliva · 2020
Closest in time.