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Deepfakes refer to content synthesized using deep generators, which, when misused, have the potential to erode trust in digital media.
The jpeg still picture compression standard
Gregory K Wallace · 1992
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Combined dwt-dct digital image watermarking
Ali Al-Haj · 2007
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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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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Scarlett johansson on fake ai-generated sex videos:‘nothing can stop someone from cutting and pasting my image’
Drew Harwell · 2018
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The upside of deep fakes
Jessica Silbey and Woodrow Hartzog · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 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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Cross-domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training
Sandy Engelhardt, Lalith Sharan, Matthias Karck, Raffaele De Simone, and Ivo Wolf · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Do gans leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
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Exploiting visual artifacts to expose deepfakes and face manipulations
Falko Matern, Christian Riess, and Marc Stamminger · 2019
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Faceforensics++: Learning to detect manipulated facial images
Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
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The emergence of deepfake technology: A review
Mika Westerlund · 2019
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Attributing fake images to gans: Learning and analyzing gan fingerprints
Ning Yu, Larry S Davis, and Mario Fritz · 2019
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Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Photographic and video deepfakes have arrived: how machine learning may influence plastic surgery
Dustin T Crystal, Nicholas G Cuccolo, Ahmed Ibrahim, Heather Furnas, and Samuel J Lin · 2020
Cited alongside, same era.
The deepfake detection challenge (dfdc) dataset
Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer · 2020
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
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Sebastian Szyller, Vasisht Duddu, Tommi Gröndahl, and N Asokan · 2021
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Understanding the capabilities, limitations, and societal impact of large language models
Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli · 2021
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Learning to disentangle gan fingerprint for fake image attribution
Tianyun Yang, Juan Cao, Qiang Sheng, Lei Li, Jiaqi Ji, Xirong Li, and Sheng Tang · 2021
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Artificial fingerprinting for generative models: Rooting deepfake attribution in training data
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz · 2021
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Repmix: Representation mixing for robust attribution of synthesized images
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Thinking in frequency: Face forgery detection by mining frequency-aware clues
Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao · 2020
Cited alongside, same era.
Creating artificial images for radiology applications using generative adversarial networks (gans)–a systematic review
Vera Sorin, Yiftach Barash, Eli Konen, and Eyal Klang · 2020
Cited alongside, same era.
Catastrophic forgetting and mode collapse in gans
Hoang Thanh-Tung and Truyen Tran · 2020
Cited alongside, same era.
Sstnet: Detecting manipulated faces through spatial, steganalysis and temporal features
Xi Wu, Zhen Xie, YuTao Gao, and Yu Xiao · 2020
Cited alongside, same era.
Responsible disclosure of generative models using scalable fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen, Larry Davis, and Mario Fritz · 2020
Cited alongside, same era.
An automated and robust image watermarking scheme based on deep neural networks
Xin Zhong, Pei-Chi Huang, Spyridon Mastorakis, and Frank Y Shih · 2020
Cited alongside, same era.
In-domain gan inversion for real image editing
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou · 2020
Cited alongside, same era.
Tu Bui, Ning Yu, and John Collomosse · 2022
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Think twice before detecting gan-generated fake images from their spectral domain imprints
Chengdong Dong, Ajay Kumar, and Eryun Liu · 2022
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Protecting celebrities from deepfake with identity consistency transformer
Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Ting Zhang, Weiming Zhang, Nenghai Yu, Dong Chen, Fang Wen, and Baining Guo · 2022
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New deepfake regulations in china are a tool for social stability, but at what cost?
Emmie Hine and Luciano Floridi · 2022
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Bihpf: bilateral high-pass filters for robust deepfake detection
Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi · 2022
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Sok: How robust is image classification deep neural network watermarking?
Nils Lukas, Edward Jiang, Xinda Li, and Florian Kerschbaum · 2022
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Deepphish: Understanding user trust towards artificially generated profiles in online social networks
Jaron Mink, Licheng Luo, Natã M Barbosa, Olivia Figueira, Yang Wang, and Gang Wang · 2022
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Deepfake knee osteoarthritis x-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification
Fabi Prezja, Juha Paloneva, Ilkka Pölönen, Esko Niinimäki, and Sami Äyrämö · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Pivotal tuning for latent-based editing of real images
Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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De-fake: Detection and attribution of fake images generated by text-to-image diffusion models
Zeyang Sha, Zheng Li, Ning Yu, and Yang Zhang · 2022
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Deepfake network architecture attribution
Tianyun Yang, Ziyao Huang, Juan Cao, Lei Li, and Xirong Li · 2022
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Styleswin: Transformer-based gan for high-resolution image generation
Bowen Zhang, Shuyang Gu, Bo Zhang, Jianmin Bao, Dong Chen, Fang Wen, Yong Wang, and Baining Guo · 2022
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Watching the big artifacts: Exposing deepfake videos via bi-granularity artifacts
Han Chen, Yuezun Li, Dongdong Lin, Bin Li, and Junqiang Wu · 2023
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Towards openness beyond open access: User journeys through 3 open ai collaboratives
Jennifer Ding, Christopher Akiki, Yacine Jernite, Anne Lee Steele, and Temi Popo · 2023
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