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Over the past years, deep generative models have achieved a new level of performance.
Robust template matching for affine resistant image watermarks
Shelby Pereira and Thierry Pun · 2000
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Digital watermarking
Ingemar Cox, Matthew Miller, Jeffrey Bloom, and Chris Honsinger · 2002
Earlier work this paper cites.
Watermarking security: theory and practice
Francois Cayre, Caroline Fontaine, and Teddy Furon · 2005
Earlier work this paper cites.
Print and scan’resilient data hiding in images
Kaushal Solanki, Upamanyu Madhow, BS Manjunath, Shiv Chandrasekaran, and Ibrahim El-Khalil · 2006
Earlier work this paper cites.
Steganography in digital media: principles, algorithms, and applications
Jessica Fridrich · 2009
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Efficient general print-scanning resilient data hiding based on uniform log-polar mapping
Xiangui Kang, Jiwu Huang, and Wenjun Zeng · 2010
Earlier work this paper cites.
Using high-dimensional image models to perform highly undetectable steganography
Tomáš Pevnỳ, Tomáš Filler, and Patrick Bas · 2010
Earlier work this paper cites.
Spatially varying radiometric calibration for camera-display messaging
Wenjia Yuan, Kristin J Dana, Ashwin Ashok, Marco Gruteser, and Narayan Mandayam · 2013
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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
Earlier work this paper cites.
Universal distortion function for steganography in an arbitrary domain
Vojtěch Holub, Jessica Fridrich, and Tomáš Denemark · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, 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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Dynamic filter networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool · 2016
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Unsupervised steganalysis based on artificial training sets
Daniel Lerch-Hostalot and David Megías · 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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Hiding images in plain sight: Deep steganography
Shumeet Baluja · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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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
Earlier work this paper cites.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
Cited alongside, same era.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
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.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
The malicious use of artificial intelligence: Forecasting, prevention, and mitigation
Do gans leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Deepsigns: an end-to-end watermarking framework for protecting the ownership of deep neural networks
Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar · 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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Detecting and simulating artifacts in gan fake images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
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Language models are few-shot learners
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Miles Brundage, Shahar Avin, Jack Clark, Helen Toner, Peter Eckersley, Ben Garfinkel, Allan Dafoe, Paul Scharre, Thomas Zeitzoff, Bobby Filar, et al · 2018
Cited alongside, same era.
Rebroadcast attacks: Defenses, reattacks, and redefenses
Wei Fan, Shruti Agarwal, and Hany Farid · 2018
Cited alongside, same era.
Screen-shooting resilient watermarking
Han Fang, Weiming Zhang, Hang Zhou, Hao Cui, and Nenghai Yu · 2018
Cited alongside, same era.
Deepfake video detection using recurrent neural networks
David Güera and Edward J Delp · 2018
Cited alongside, same era.
Deepfakes: False pornography is here and the law cannot protect you
Douglas Harris · 2018
Cited alongside, same era.
Learning to detect fake face images in the wild
Chih-Chung Hsu, Chia-Yen Lee, and Yi-Xiu Zhuang · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Closest in time.
Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions
Ricard Durall, Margret Keuper, and Janis Keuper · 2020
Closest in time.
Leveraging frequency analysis for deep fake image recognition
Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz · 2020
Closest in time.
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
Closest in time.
Distortion agnostic deep watermarking
Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, and Peyman Milanfar · 2020
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Updates-leak: Data set inference and reconstruction attacks in online learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
Closest in time.
Stegastamp: Invisible hyperlinks in physical photographs
Matthew Tancik, Ben Mildenhall, and Ren Ng · 2020
Closest in time.
Cnn-generated images are surprisingly easy to spot… for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
Closest in time.
Inclusive gan: Improving data and minority coverage in generative models
Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz · 2020
Closest in time.
Not my deepfake: Towards plausible deniability for machine-generated media
Baiwu Zhang, Jin Peng Zhou, Ilia Shumailov, and Nicolas Papernot · 2020
Closest in time.
Reverse engineering of generative models: Inferring model hyperparameters from generated images
Vishal Asnani, Xi Yin, Tal Hassner, and Xiaoming Liu · 2021
Closest in time.
Towards discovery and attribution of open-world gan generated images
Sharath Girish, Saksham Suri, Saketh Rambhatla, and Abhinav Shrivastava · 2021
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Protecting intellectual property of generative adversarial networks from ambiguity attacks
Ding Sheng Ong, Chee Seng Chan, Kam Woh Ng, Lixin Fan, and Qiang Yang · 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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