Fetching the paper…
Reading the bibliography…
Rapid pace of generative models has brought about new threats to visual forensics such as malicious personation and digital copyright infringement, which promotes works on fake image attribution.
Textural features for image classification
Robert M Haralick, Karthikeyan Shanmugam, and Its’ Hak Dinstein · 1973
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Photographic image synthesis with cascaded refinement networks
Qifeng Chen and Vladlen Koltun · 2017
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Earlier work this paper cites.
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.
Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
Earlier work this paper cites.
Learning to see in the dark
Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Detecting and simulating artifacts in gan fake images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
Later among the works it cites.
What makes fake images detectable? understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola · 2020
Later among the works it cites.
Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions
Ricard Durall, Margret Keuper, and Janis Keuper · 2020
Later among the works it cites.
Leveraging frequency analysis for deep fake image recognition
Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz · 2020
Later among the works it cites.
T-gd: Transferable gan-generated images detection framework
Hyeonseong Jeon, Young Oh Bang, Junyaup Kim, and Simon Woo · 2020
Later among the works it cites.
Analyzing and improving the image quality of stylegan
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Diverse image synthesis from semantic layouts via conditional imle
Ke Li, Tianhao Zhang, and Jitendra Malik · 2019
Cited alongside, same era.
Do gans leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
Cited alongside, same era.
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.
Gaugan: semantic image synthesis with spatially adaptive normalization
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu · 2019
Cited alongside, same era.
Faceforensics++: Learning to detect manipulated facial images
Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 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.
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Later among the works it cites.
Decentralized attribution of generative models
Changhoon Kim, Yi Ren, and Yezhou Yang · 2020
Later among the works it cites.
Global texture enhancement for fake face detection in the wild
Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr · 2020
Later among the works it cites.
Cnn-generated images are surprisingly easy to spot… for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
Later among the works it cites.
Artificial gan fingerprints: Rooting deepfake attribution in training data
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz · 2020
Later among the works it cites.
Responsible disclosure of generative models using scalable fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen, Larry Davis, and Mario Fritz · 2020
Later among the works it cites.
Infomax-gan: Improved adversarial image generation via information maximization and contrastive learning
Kwot Sin Lee, Ngoc-Trung Tran, and Ngai-Man Cheung · 2021
Closest in time.