Fetching the paper…
Reading the bibliography…
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
Poisson image editing
Patrick Pérez, Michel Gangnet, and Andrew Blake · 2003
Earlier work this paper cites.
Exposing digital forgeries by detecting traces of resampling
Alin C Popescu and Hany Farid · 2005
Earlier work this paper cites.
Using color compatibility for assessing image realism
Jean-François Lalonde and Alexei A. Efros · 2007
Earlier work this paper cites.
Learning OpenCV: Computer vision with the OpenCV library
Gary Bradski and Adrian Kaehler · 2008
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
Earlier work this paper cites.
Exposing photo manipulation with inconsistent reflections
James F O’Brien and Hany Farid · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Splicebuster: A new blind image splicing detector
Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning a discriminative model for the perception of realism in composite images
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros · 2015
Earlier work this paper cites.
Photo forensics
Hany Farid · 2016
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.
Deconvolution and checkerboard artifacts
Augustus Odena, Vincent Dumoulin, and Chris Olah · 2016
Earlier work this paper cites.
A deep learning approach to detection of splicing and copy-move forgeries in images
Yuan Rao and Jiangqun Ni · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Photo forensics from jpeg dimples
Shruti Agarwal and Hany Farid · 2017
Cited alongside, same era.
Photographic image synthesis with cascaded refinement networks
Qifeng Chen and Vladlen Koltun · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
Closest in time.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Closest in time.
Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
Closest in time.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Closest in time.
Diverse image synthesis from semantic layouts via conditional imle
Ke Li, Tianhao Zhang, and Jitendra Malik · 2019
Closest in time.
Do gans leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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.
Forensictransfer: Weakly-supervised domain adaptation for forgery detection
Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva · 2018
Cited alongside, same era.
Fighting fake news: Image splice detection via learned self-consistency
Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros · 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.
Detection of gan-generated fake images over social networks
Francesco Marra, Diego Gragnaniello, Davide Cozzolino, and Luisa Verdoliva · 2018
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
Closest in time.
Semantic image synthesis with spatially-adaptive normalization
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu · 2019
Closest in time.
FaceForensics++: Learning to detect manipulated facial images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2019
Closest in time.
Fakespotter: A simple baseline for spotting ai-synthesized fake faces
Run Wang, Lei Ma, Felix Juefei-Xu, Xiaofei Xie, Jian Wang, and Yang Liu · 2019
Closest in time.
Detecting photoshopped faces by scripting photoshop
Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, and Alexei A Efros · 2019
Closest in time.
Attributing fake images to gans: Analyzing fingerprints in generated images
Ning Yu, Larry Davis, and Mario Fritz · 2019
Closest in time.
Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2019
Closest in time.
Making convolutional networks shift-invariant again
Richard Zhang · 2019
Closest in time.
Detecting and simulating artifacts in gan fake images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
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
Closest in time.