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In the burgeoning age of generative AI, watermarks act as identifiers of provenance and artificial content.
Entangled watermarks as a defense against model extraction
Jia, H., Choquette-Choo, C. A., Chandrasekaran, V., and Papernot, N · 1954
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Secure spread spectrum watermarking for images, audio and video
Cox, I. J., Kilian, J., Leighton, T., and Shamoon, T · 1996
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Watermarking digital images for copyright protection
ó Ruanaidh, J., Dowling, W., and Boland, F · 1996
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Rotation, scale and translation invariant digital image watermarking
O’Ruanaidh, J. J. and Pun, T · 1997
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Fair benchmark for image watermarking systems
Kutter, M. and Petitcolas, F. A · 1999
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Watermarking schemes evaluation
Petitcolas, F. A · 2000
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Svd-based digital image watermarking scheme
Chang, C.-C., Tsai, P., and Lin, C.-C · 2005
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Combined dwt-dct digital image watermarking
Al-Haj, A · 2007
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Digital watermarking and steganography
Cox, I., Miller, M., Bloom, J., Fridrich, J., and Kalker, T · 2007
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Robust image watermarking theories and techniques: A review
Tao, H., Chongmin, L., Zain, J. M., and Abdalla, A. N · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Generating steganographic images via adversarial training
Hayes, J. and Danezis, G · 2017
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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
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Variational image compression with a scale hyperprior
Ballé, J., Minnen, D., Singh, S., Hwang, S. J., and Johnston, N · 2018
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Adversarial attacks and defences: A survey
Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., and Mukhopadhyay, D · 2018
Cited alongside, same era.
Deepsigns: A generic watermarking framework for ip protection of deep learning models
Rouhani, B. D., Chen, H., and Koushanfar, F · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Cited alongside, same era.
Hidden: Hiding data with deep networks
Zhu, J., Kaplan, R., Johnson, J., and Fei-Fei, L · 2018
Cited alongside, same era.
Deepmarks: A secure fingerprinting framework for digital rights management of deep learning models
Chen, H., Rouhani, B. D., Fu, C., Zhao, J., and Koushanfar, F · 2019
Cited alongside, same era.
The role of imagenet classes in fr \ \backslash ’echet inception distance
Kynkäänniemi, T., Karras, T., Aittala, M., Aila, T., and Lehtinen, J · 2022
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Diffusion models for adversarial purification
Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., and Anandkumar, A · 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
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Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models
Wang, Z. J., Montoya, E., Munechika, D., Yang, H., Hoover, B., and Chau, D. H · 2022
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Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
Cited alongside, same era.
Feature space perturbations yield more transferable adversarial examples
Inkawhich, N., Wen, W., Li, H. H., and Chen, Y · 2019
Cited alongside, same era.
Robust invisible video watermarking with attention
Zhang, K. A., Xu, L., Cuesta-Infante, A., and Veeramachaneni, K · 2019
Cited alongside, same era.
Redmark: Framework for residual diffusion watermarking based on deep networks
Ahmadi, M., Norouzi, A., Karimi, N., Samavi, S., and Emami, A · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Stegastamp: Invisible hyperlinks in physical photographs
Tancik, M., Mildenhall, B., and Ng, R · 2020
Cited alongside, same era.
Dong, Y., Chen, H., Chen, J., Fang, Z., Yang, X., Zhang, Y., Tian, Y., Su, H., and Zhu, J · 2023
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Executive order 14110: Safe, secure, and trustworthy development and use of artificial intelligence
Executive Office of the President · 2023
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The stable signature: Rooting watermarks in latent diffusion models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., and Furon, T · 2023
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Evading watermark based detection of ai-generated content
Jiang, Z., Zhang, J., and Gong, N. Z · 2023
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Ptw: Pivotal tuning watermarking for pre-trained image generators
Lukas, N. and Kerschbaum, F · 2023
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Leveraging optimization for adaptive attacks on image watermarks
Lukas, N., Diaa, A., Fenaux, L., and Kerschbaum, F · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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Robustness of ai-image detectors: Fundamental limits and practical attacks
Saberi, M., Sadasivan, V. S., Rezaei, K., Kumar, A., Chegini, A., Wang, W., and Feizi, S · 2023
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Tree-ring watermarks: Fingerprints for diffusion images that are invisible and robust
Wen, Y., Kirchenbauer, J., Geiping, J., and Goldstein, T · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., and Dong, Y · 2023
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Securing deep generative models with universal adversarial signature
Zeng, Y., Zhou, M., Xue, Y., and Patel, V. M · 2023
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