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In the course of the past few years, diffusion models (DMs) have reached an unprecedented level of visual quality.
The JPEG still picture compression standard
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Natural Image Statistics in Digital Image Forensics
Lyu, S. (2008) · 2008
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Visualizing data using t-SNE
van der Maaten, L. and Hinton, G. (2008) · 2008
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
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Scheduled denoising autoencoders
Geras, K. J. and Sutton, C. (2015) · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Composite denoising autoencoders
Geras, K. J. and Sutton, C. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J. (2016) · 2016
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
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Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2018) · 2018
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T. (2019) · 2019
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Do GANs leave artificial fingerprints?
Marra, F., Gragnaniello, D., Verdoliva, L., and Poggi, G. (2019) · 2019
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Detecting GAN-generated imagery using saturation cues
McCloskey, S. and Albright, M. (2019) · 2019
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Detecting GAN generated fake images using co-occurrence matrices
Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., and Roy-Chowdhury, A. K. (2019) · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q. (2019) · 2019
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On the generalization of GAN image forensics
Xuan, X., Peng, B., Wang, W., and Dong, J. (2019) · 2019
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Detecting and simulating artifacts in GAN fake images
Zhang, X., Karaman, S., and Chang, S.-F. (2019) · 2019
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What makes fake images detectable? Understanding properties that generalize
Chai, L., Bau, D., Lim, S.-N., and Isola, P. (2020) · 2020
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Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions
Durall, R., Keuper, M., and Keuper, J. (2020) · 2020
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Fourier spectrum discrepancies in deep network generated images
Dzanic, T., Shah, K., and Witherden, F. (2020) · 2020
Cited alongside, same era.
Leveraging frequency analysis for deep fake image recognition
Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., and Holz, T. (2020) · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
Cited alongside, same era.
Detecting CNN-generated facial images in real-world scenarios
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I. (2021) · 2021
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Projected GANs converge faster
Sauer, A., Chitta, K., Müller, J., and Geiger, A. (2021) · 2021
Later among the works it cites.
On the frequency bias of generative models
Schwarz, K., Liao, Y., and Geiger, A. (2021) · 2021
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Perception prioritized training of diffusion models
Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., and Yoon, S. (2022) · 2022
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FingerprintNet: Synthesized fingerprints for generated image detection
Jeong, Y., Kim, D., Ro, Y., Kim, P., and Choi, J. (2022) · 2022
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Spatial frequency bias in convolutional generative adversarial networks
Khayatkhoei, M. and Elgammal, A. (2022) · 2022
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Hulzebosch, N., Ibrahimi, S., and Worring, M. (2020) · 2020
Cited alongside, same era.
Analyzing and improving the image quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020) · 2020
Cited alongside, same era.
Global texture enhancement for fake face detection in the wild
Liu, Z., Qi, X., and Torr, P. H. S. (2020) · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S. (2020) · 2020
Cited alongside, same era.
Media forensics and DeepFakes: An overview
Verdoliva, L. (2020) · 2020
Cited alongside, same era.
CNN-generated images are surprisingly easy to spot… for now
Wang, S.-Y., Wang, O., Zhang, R., Owens, A., and Efros, A. A. (2020) · 2020
Cited alongside, same era.
A closer look at Fourier spectrum discrepancies for CNN-generated images detection
Chandrasegaran, K., Tran, N.-T., and Cheung, N.-M. (2021) · 2021
Cited alongside, same era.
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Pseudo numerical methods for diffusion models on manifolds
Liu, L., Ren, Y., Lin, Z., and Zhao, Z. (2022) · 2022
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Detecting GAN-generated images by orthogonal training of multiple CNNs
Mandelli, S., Bonettini, N., Bestagini, P., and Tubaro, S. (2022) · 2022
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AI-synthesized faces are indistinguishable from real faces and more trustworthy
Nightingale, S. J. and Farid, H. (2022) · 2022
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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022) · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M. (2022) · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J. (2022) · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A. (2022) · 2022
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Why Pope Francis is the star of A.I.-generated photos
Huang, K. (2023) · 2023
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Understanding diffusion objectives as the ELBO with simple data augmentation
Kingma, D. P. and Gao, R. (2023) · 2023
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Towards universal fake image detectors that generalize across generative models
Ojha, U., Li, Y., and Lee, Y. J. (2023) · 2023
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Generative modelling with inverse heat dissipation
Rissanen, S., Heinonen, M., and Solin, A. (2023) · 2023
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DE-FAKE: Detection and attribution of fake images generated by text-to-image diffusion models
Sha, Z., Li, Z., Yu, N., and Zhang, Y. (2023) · 2023
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DIRE for diffusion-generated image detection
Wang, Z., Bao, J., Zhou, W., Wang, W., Hu, H., Chen, H., and Li, H. (2023) · 2023
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Diffusion models: A comprehensive survey of methods and applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., and Yang, M.-H. (2023) · 2023
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