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The rapid advances in deep generative models over the past years have led to highly {realistic media, known as deepfakes,} that are commonly indistinguishable from real to human eyes.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, 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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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 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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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 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
Earlier work this paper cites.
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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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Detection of gan-generated fake images over social networks
Francesco Marra, Diego Gragnaniello, Davide Cozzolino, and Luisa Verdoliva · 2018
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High-resolution image synthesis and semantic manipulation with conditional gans
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Earlier work this paper cites.
Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
Cited alongside, same era.
Unmasking deepfakes with simple features
Ricard Durall, Margret Keuper, Franz-Josef Pfreundt, and Janis Keuper · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Cited alongside, same era.
Do gans leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
Cited alongside, same era.
A full-image full-resolution end-to-end-trainable cnn framework for image forgery detection
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
Cited alongside, same era.
Leveraging frequency analysis for deep fake image recognition
Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Global texture enhancement for fake face detection in the wild
Zhengzhe Liu, Xiaojuan Qi, Jiaya Jia, and Philip Torr · 2020
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Two-branch recurrent network for isolating deepfakes in videos
Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed · 2020
Later among the works it cites.
Thinking in frequency: Face forgery detection by mining frequency-aware clues
Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao · 2020
Later among the works it cites.
Cnn-generated images are surprisingly easy to spot… for now
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Detecting gan generated fake images using co-occurrence matrices
Lakshmanan Nataraj, Tajuddin Manhar Mohammed, BS Manjunath, Shivkumar Chandrasekaran, Arjuna Flenner, Jawadul H Bappy, and Amit K Roy-Chowdhury · 2019
Cited alongside, same era.
Faceforensics++: Learning to detect manipulated facial images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
Cited alongside, same era.
Texture mixer: A network for controllable synthesis and interpolation of texture
Ning Yu, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, and Michal Lukac · 2019
Cited alongside, same era.
Attributing fake images to gans: Learning and analyzing gan fingerprints
Ning Yu, Larry S Davis, and Mario Fritz · 2019
Cited alongside, same era.
Detecting and simulating artifacts in gan fake images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
Cited alongside, same era.
What makes fake images detectable? understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola · 2020
Cited alongside, same era.
Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
Cited alongside, same era.
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
Later among the works it cites.
Inclusive gan: Improving data and minority coverage in generative models
Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz · 2020
Later among the works it cites.
Not my deepfake: Towards plausible deniability for machine-generated media
Baiwu Zhang, Jin Peng Zhou, Ilia Shumailov, and Nicolas Papernot · 2020
Later among the works it cites.
Spectral distribution aware image generation
Steffen Jung and Margret Keuper · 2021
Closest in time.
Hijack-gan: Unintended-use of pretrained, black-box gans
Hui-Po Wang, Ning Yu, and Mario Fritz · 2021
Closest in time.
Dual contrastive loss and attention for gans
Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, and Mario Fritz · 2021
Closest in time.
Artificial fingerprinting for generative models: Rooting deepfake attribution in training data
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz · 2021
Closest in time.
Responsible disclosure of generative models using scalable fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen, Larry Davis, and Mario Fritz · 2021
Closest in time.