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The advent of deep learning has brought a significant improvement in the quality of generated media.
“Microsoft COCO: Common objects in context,”
T.-Y et al. Lin, · 2014
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“RAISE: A raw images dataset for digital image forensics,”
D.-T. Dang-Nguyen, C. Pasquini, V. Conotter, and G. Boato, · 2015
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“Unpaired image-to-image translation using cycle-consistent adversarial networks,”
J.-Y. Zhu, T. Park, P. Isola, and A. Efros, · 2017
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“Detection of GAN-generated fake images over social networks,”
F. Marra, D. Gragnaniello, D. Cozzolino, and L. Verdoliva, · 2018
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“ForensicTransfer: Weakly-supervised domain adaptation for forgery detection,”
D. Cozzolino, J. Thies, A. Rössler, C. Riess, M. Nießner, and L. Verdoliva, · 2018
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“Progressive Growing of GANs for Improved Quality, Stability, and Variation,”
T. Karras, T. Aila, S. Laine, and J. Lehtinen, · 2018
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“Large Scale GAN Training for High Fidelity Natural Image Synthesis,”
A. Brock, J. Donahue, and K. Simonyan, · 2018
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“StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation,”
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, · 2018
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“Do GANs Leave Artificial Fingerprints?,”
F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, · 2019
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“Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints,”
N. Yu, L. Davis, and M. Fritz, · 2019
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“Detecting GAN-Generated Imagery Using Saturation Cues,”
S. McCloskey and M. Albright, · 2019
Cited alongside, same era.
“Detecting GAN generated fake images using co-occurrence matrices,”
L. Nataraj et al., · 2019
Cited alongside, same era.
“Detecting and Simulating Artifacts in GAN Fake Images,”
X. Zhang, S. Karaman, and S.-F. Chang, · 2019
Cited alongside, same era.
“Towards generalizable forgery detection with locality-aware autoencoder,”
M. Du, S. Pentyala, Y. Li, and X. Hu, · 2019
Cited alongside, same era.
“Incremental learning for the detection and classification of gan-generated images,”
F. Marra, C. Saltori, G. Boato, and L. Verdoliva, · 2019
Cited alongside, same era.
“On the generalization of GAN image forensics,”
X. Xuan, B. Peng, W. Wang, and J. Dong, · 2019
Cited alongside, same era.
“Media forensics and deepfakes: an overview,”
L. Verdoliva, · 2020
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“Detection of deep network generated images using disparities in color components,”
H. Li, B. Li, S. Tan, and J. Huang, · 2020
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“CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis,”
M. Barni, K. Kallas, E. Nowroozi, and B. Tondi, · 2020
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“Leveraging Frequency Analysis for Deep Fake Image Recognition,”
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, and T. Holz, · 2020
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“Watch your up-convolution: CNN based Generative Deep Neural Networks are failing to reproduce spectral distributions,”
R. Durall, M. Keuper, and J. Keuper, · 2020
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“CNN-generated images are surprisingly easy to spot… for now,”
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“A style-based generator architecture for generative adversarial networks,”
T. Karras, S. Laine, and T. Aila, · 2019
Cited alongside, same era.
“RelGAN: Multi-domain image-to-image translation via relative attributes,”
P.-W. Wu, Y.-J. Lin, H. Chang C, E. Chang, and S.-W. Liao, · 2019
Cited alongside, same era.
“Semantic image synthesis with spatially-adaptive normalization,”
T. Park, M.-Y. Liu, and T.-C. Wang J.-Y. Zhu, · 2019
Cited alongside, same era.
“Deep residual network for steganalysis of digital images,”
M. Boroumand, M. Chen, and J. Fridrich, · 2019
Cited alongside, same era.
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. Efros, · 2020
Later among the works it cites.
“What makes fake images detectable? Understanding properties that generalize,”
L. Chai, D. Bau, S.-N. Lim, and P. Isola, · 2020
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“Analyzing and improving the image quality of StyleGAN,”
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, · 2020
Later among the works it cites.
“ImageNet Pre-trained CNNs for JPEG Steganalysis,”
Y. Yousfi, J. Butora, E. Khvedchenya, and J. Fridrich, · 2020
Later among the works it cites.
“A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection,”
F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, · 2020
Later among the works it cites.