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Reports regarding the misuse of Generative AI (GenAI) to create deepfakes are frequent.
S. Pereira and T. Pun, “Robust template matching for affine resistant image watermarks,” IEEE Transactions on Image Processing , 2000
2000
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N. Bi, Q. Sun, D. Huang, Z. Yang, and J. Huang, “Robust image watermarking based on multiband wavelets and empirical mode decomposition,” IEEE Transactions on Image Processing , 2007
2007
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D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in ICLR , 2014
2014
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” NeurIPS , 2014
2014
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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
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J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in ICML , 2015
2015
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” NeurIPS , 2017
2017
Earlier work this paper cites.
D. Harwell, “Scarlett Johansson on fake AI-generated sex videos: “nothing can stop someone from cutting and pasting my image”,” https://www.washingtonpost.com/technology/2018/12/31/scarlett-johansson-fake-ai-generated-sex-videos-nothing-can-stop-someone-cutting-pasting-my-image/ , 2018
2018
Earlier work this paper cites.
J. Zhu, R. Kaplan, J. Johnson, and L. Fei-Fei, “HiDDeN: Hiding data with deep networks,” in ECCV , 2018
2018
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R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in CVPR , 2018
2018
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T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in ICLR , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
F. Tramèr, A. Kurakin, N. Papernot, I. J. Goodfellow, D. Boneh, and P. D. McDaniel, “Ensemble adversarial training: Attacks and defenses,” in ICLR , 2018
2018
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S. Engelhardt, L. Sharan, M. Karck, R. D. Simone, and I. Wolf, “Cross-domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2019
2019
Earlier work this paper cites.
S. Agarwal, H. Farid, Y. Gu, M. He, K. Nagano, and H. Li, “Protecting world leaders against deep fakes.” in CVPR workshops , 2019
2019
Earlier work this paper cites.
F. Matern, C. Riess, and M. Stamminger, “Exploiting visual artifacts to expose deepfakes and face manipulations,” in WACVW , 2019
2019
Earlier work this paper cites.
F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, “Do GANs leave artificial fingerprints?” in 2019 IEEE conference on multimedia information processing and retrieval (MIPR) , 2019
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in CVPR , 2019
2019
Earlier work this paper cites.
V. Sorin, Y. Barash, E. Konen, and E. Klang, “Creating artificial images for radiology applications using generative adversarial networks (GANs)–a systematic review,” Academic radiology , 2020
2020
Earlier work this paper cites.
D. T. Crystal, N. G. Cuccolo, A. Ibrahim, H. Furnas, and S. J. Lin, “Photographic and video deepfakes have arrived: how machine learning may influence plastic surgery,” Plastic and reconstructive surgery , 2020
2020
Earlier work this paper cites.
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, “Face X-ray for more general face forgery detection,” in CVPR , 2020
2020
Cited alongside, same era.
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, and T. Holz, “Leveraging frequency analysis for deep fake image recognition,” in ICML , 2020
2020
Cited alongside, same era.
M. Tancik, B. Mildenhall, and R. Ng, “Stegastamp: Invisible hyperlinks in physical photographs,” in CVPR , 2020
2020
Cited alongside, same era.
Y. Qian, G. Yin, L. Sheng, Z. Chen, and J. Shao, “Thinking in frequency: Face forgery detection by mining frequency-aware clues,” in European conference on computer vision , 2020
2020
Cited alongside, same era.
S. Czolbe, O. Krause, I. Cox, and C. Igel, “A loss function for generative neural networks based on Watson’s perceptual model,” NeurIPS , 2020
2020
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022
2022
Later among the works it cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” NeurIPS , 2022
2022
Later among the works it cites.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” NeurIPS , 2022
2022
Later among the works it cites.
W. Nie, B. Guo, Y. Huang, C. Xiao, A. Vahdat, and A. Anandkumar, “Diffusion models for adversarial purification,” in ICML , 2022
2022
Later among the works it cites.
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Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” in CVPR , 2020
2020
Cited alongside, same era.
A. De Ruiter, “The distinct wrong of deepfakes,” Philosophy & Technology , 2021
2021
Cited alongside, same era.
H. Zhao, W. Zhou, D. Chen, T. Wei, W. Zhang, and N. Yu, “Multi-attentional deepfake detection,” in CVPR , 2021
2021
Cited alongside, same era.
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, and T. Aila, “Alias-free generative adversarial networks,” NeurIPS , 2021
2021
Cited alongside, same era.
N. Yu, V. Skripniuk, S. Abdelnabi, and M. Fritz, “Artificial fingerprinting for generative models: Rooting deepfake attribution in training data,” in ICCV , 2021
2021
Cited alongside, same era.
S. Hu, Y. Li, and S. Lyu, “Exposing GAN-generated faces using inconsistent corneal specular highlights,” in ICASSP , 2021
2021
Cited alongside, same era.
X. Cao and N. Z. Gong, “Understanding the security of deepfake detection,” in International Conference on Digital Forensics and Cyber Crime , 2021
2021
Cited alongside, same era.
R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On the detection of synthetic images generated by diffusion models,” in ICASSP , 2023
2023
Later among the works it cites.
Y. Wen, J. Kirchenbauer, J. Geiping, and T. Goldstein, “Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images,” in NeurIPS , 2023
2023
Later among the works it cites.
P. Fernandez, G. Couairon, H. Jégou, M. Douze, and T. Furon, “The stable signature: Rooting watermarks in latent diffusion models,” ICCV , 2023
2023
Later among the works it cites.
N. Lukas and F. Kerschbaum, “PTW: Pivotal tuning watermarking for Pre-Trained image generators,” in USENIX Security 23 , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Gowal, “Identifying AI-generated images with Synthid,” https://deepmind.google/discover/blog/identifying-ai-generated-images-with-synthid/ , 2023
2023
Later among the works it cites.
A. Belanger, “OpenAI, Google will watermark AI-generated content to hinder deepfakes, misinfo,” https://arstechnica.com/ai/2023/07/openai-google-will-watermark-ai-generated-content-to-hinder-deepfakes-misinfo/ , 2023
2023
Later among the works it cites.
M. Heikkilä;, “Google deepmind has launched a watermarking tool for ai-generated images,” https://www.technologyreview.com/2023/08/29/1078620/google-deepmind-has-launched-a-watermarking-tool-for-ai-generated-images/ , 2023
2023
Later among the works it cites.
Z. Jiang, J. Zhang, and N. Z. Gong, “Evading watermark based detection of AI-generated content,” in CCS , 2023
2023
Later among the works it cites.
X. Zhao, K. Zhang, Z. Su, S. Vasan, I. Grishchenko, C. Kruegel, G. Vigna, Y.-X. Wang, and L. Li, “Invisible image watermarks are provably removable using generative AI,” in ICML 2023 Workshop on Challenges in Deploying Generative AI , 2023
2023
Later among the works it cites.
A. Kassis and U. Hengartner, “Breaking security-critical voice authentication,” in 2023 IEEE Symposium on Security and Privacy (SP) , 2023
2023
Later among the works it cites.
M. Saberi, V. S. Sadasivan, K. Rezaei, A. Kumar, A. Chegini, W. Wang, and S. Feizi, “Robustness of AI-image detectors: Fundamental limits and practical attacks,” in ICLR , 2024
2024
Closest in time.
B. An, M. Ding, T. Rabbani, A. Agrawal, Y. Xu, C. Deng, S. Zhu, A. Mohamed, Y. Wen, T. Goldstein, and F. Huang, “WAVES: benchmarking the robustness of image watermarks,” in ICML , 2024
2024
Closest in time.
N. Lukas, A. Diaa, L. Fenaux, and F. Kerschbaum, “Leveraging optimization for adaptive attacks on image watermarks,” in ICLR , 2024
2024
Closest in time.
2024
Closest in time.
E. David and A. Heath, “Meta says you better disclose your AI fakes or it might just pull them,” https://www.theverge.com/2024/2/6/24062388/meta-ai-photo-watermark-facebook-instagram-threads , 2024
2024
Closest in time.