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Trained on massive amounts of human-generated content, AI-generated image synthesis is capable of reproducing semantically coherent images that match the visual appearance of its training data.
A review of watermarking principles and practices
Ingemar J Cox, Matt L Miller, JMG Linnartz, and Ton Kalker · 1999
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Détection et classification de visages pour la description de l’égalité femme-homme dans les archives télévisuelles, 2019
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Singan: Learning a generative model from a single natural image
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Detecting and simulating artifacts in GAN fake images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
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What makes fake images detectable? Understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola · 2020
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Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2021
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Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Diego Gragnaniello, Davide Cozzolino, Francesco Marra, Giovanni Poggi, and Luisa Verdoliva · 2021
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CLIPScore: A reference-free evaluation metric for image captioning
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Improved denoising diffusion probabilistic models
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Learning transferable visual models from natural language supervision
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HyperExtended lightface: A facial attribute analysis framework
Sefik Ilkin Serengil and Alper Ozpinar · 2021
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LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Self-consuming generative models go mad
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard G Baraniuk · 2023
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Poisoning web-scale training datasets is practical
Nicholas Carlini, Matthew Jagielski, Christopher A Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, and Florian Tramèr · 2023
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On the detection of synthetic images generated by diffusion models
Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva · 2023
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Will large-scale generative models corrupt future datasets?
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WIT: Wikipedia-based image text dataset for multimodal multilingual machine learning
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Artificial fingerprinting for generative models: Rooting deepfake attribution in training data
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz · 2021
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Detecting generated images by real images
Bo Liu, Fan Yang, Xiuli Bi, Bin Xiao, Weisheng Li, and Xinbo Gao · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, and Rik Sarkar
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Towards understanding the interplay of generative artificial intelligence and the internet
Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, and Rik Sarkar
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Ryuichiro Hataya, Han Bao, and Hiromi Arai · 2023
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The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson · 2023
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AI models collapse when trained on recursively generated data
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal · 2024
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When AI eats itself: On the caveats of data pollution in the era of generative ai
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Text-to-image synthesis: A decade survey
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