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With the rapid advancement of diffusion-based image-generative models, the quality of generated images has become increasingly photorealistic.
W. J. Youden, “Index for rating diagnostic tests,” Cancer , vol. 3, no. 1, pp. 32–35, 1950
1950
Earlier work this paper cites.
E. Parzen, “On estimation of a probability density function and mode,” The annals of mathematical statistics , vol. 33, no. 3, pp. 1065–1076, 1962
1962
Earlier work this paper cites.
C. A. Choquette-Choo, F. Tramer, N. Carlini, and N. Papernot, “Label-only membership inference attacks,” in International conference on machine learning . PMLR, 2021, pp. 1964–1974
1974
Earlier work this paper cites.
A. B. Owen, “Monte carlo theory, methods and examples,” 2013
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” 2014
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, “Microsoft coco: Common objects in context,” 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security , 2016, pp. 308–318
2016
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” Journal of Privacy and Confidentiality , vol. 7, no. 3, pp. 17–51, 2016
2016
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” 2017
2017
Earlier work this paper cites.
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes, “Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,” 2018
2018
Earlier work this paper cites.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in 2018 IEEE 31st computer security foundations symposium (CSF) . IEEE, 2018, pp. 268–282
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Sablayrolles, M. Douze, Y. Ollivier, C. Schmid, and H. Jégou, “White-box vs black-box: Bayes optimal strategies for membership inference,” 2019
2019
Earlier work this paper cites.
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei, “Demystifying membership inference attacks in machine learning as a service,” IEEE transactions on services computing , vol. 14, no. 6, pp. 2073–2089, 2019
2019
Earlier work this paper cites.
B. Hilprecht, M. Härterich, and D. Bernau, “Monte carlo and reconstruction membership inference attacks against generative models,” Proceedings on Privacy Enhancing Technologies , 2019
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” 2020
2020
Earlier work this paper cites.
D. Chen, N. Yu, Y. Zhang, and M. Fritz, “Gan-leaks: A taxonomy of membership inference attacks against generative models,” in Proceedings of the 2020 ACM SIGSAC conference on computer and communications security , 2020, pp. 343–362
2020
Earlier work this paper cites.
Y. Long, L. Wang, D. Bu, V. Bindschaedler, X. Wang, H. Tang, C. A. Gunter, and K. Chen, “A pragmatic approach to membership inferences on machine learning models,” in 2020 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2020, pp. 521–534
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” 2020
2020
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Earlier work this paper cites.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” 2021
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
S. Rezaei and X. Liu, “On the difficulty of membership inference attacks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7892–7900
2021
Cited alongside, same era.
L. Song and P. Mittal, “Systematic evaluation of privacy risks of machine learning models,” in 30th USENIX Security Symposium (USENIX Security 21) , 2021, pp. 2615–2632
2021
Cited alongside, same era.
Y. Jiang, Z. Huang, X. Pan, C. C. Loy, and Z. Liu, “Talk-to-edit: Fine-grained facial editing via dialog,” in Proceedings of International Conference on Computer Vision (ICCV) , 2021
2021
Cited alongside, same era.
S. Gu, D. Chen, J. Bao, F. Wen, B. Zhang, D. Chen, L. Yuan, and B. Guo, “Vector quantized diffusion model for text-to-image synthesis,” 2022
2022
Later among the works it cites.
H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” 2022
2022
Later among the works it cites.
Y. Li, G. Yuan, Y. Wen, J. Hu, G. Evangelidis, S. Tulyakov, Y. Wang, and J. Ren, “Efficientformer: Vision transformers at mobilenet speed,” 2022
2022
Later among the works it cites.
J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu, “Are diffusion models vulnerable to membership inference attacks?” in International Conference on Machine Learning . PMLR, 2023, pp. 8717–8730
2023
Closest in time.
2023
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K. Srinivasan, K. Raman, J. Chen, M. Bendersky, and M. Najork, “Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 2443–2449
2021
Cited alongside, same era.
J. Li, N. Li, and B. Ribeiro, “Membership inference attacks and defenses in classification models,” in Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy , 2021, pp. 5–16
2021
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” 2021
2021
Cited alongside, same era.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in International conference on machine learning . PMLR, 2021, pp. 10 347–10 357
2021
Cited alongside, same era.
M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang, “When machine unlearning jeopardizes privacy,” in Proceedings of the 2021 ACM SIGSAC conference on computer and communications security , 2021, pp. 896–911
2021
Cited alongside, same era.
B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans, “Revisiting membership inference under realistic assumptions,” 2021
2021
Cited alongside, same era.
R. Shokri, M. Strobel, and Y. Zick, “On the privacy risks of model explanations,” 2021
2021
Cited alongside, same era.
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” 2022
2022
Cited alongside, same era.
Closest in time.
2023
Closest in time.
2023
Closest in time.
S. Shan, J. Cryan, E. Wenger, H. Zheng, R. Hanocka, and B. Y. Zhao, “Glaze: Protecting artists from style mimicry by { \{ Text-to-Image } \} models,” in 32nd USENIX Security Symposium (USENIX Security 23) , 2023, pp. 2187–2204
2023
Closest in time.
2023
Closest in time.
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramer, B. Balle, D. Ippolito, and E. Wallace, “Extracting training data from diffusion models,” in 32nd USENIX Security Symposium (USENIX Security 23) , 2023, pp. 5253–5270
2023
Closest in time.
T. Matsumoto, T. Miura, and N. Yanai, “Membership inference attacks against diffusion models,” 2023
2023
Closest in time.
W. Fu, H. Wang, C. Gao, G. Liu, Y. Li, and T. Jiang, “A probabilistic fluctuation based membership inference attack for diffusion models,” arXiv e-prints , pp. arXiv–2308, 2023
2023
Closest in time.
F. Kong, J. Duan, R. Ma, H. Shen, X. Zhu, X. Shi, and K. Xu, “An efficient membership inference attack for the diffusion model by proximal initialization,” 2023
2023
Closest in time.
2023
Closest in time.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742
2023
Closest in time.
2023
Closest in time.
S.-Y. Chou, P.-Y. Chen, and T.-Y. Ho, “Villandiffusion: A unified backdoor attack framework for diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Zhang, N. Yu, R. Wen, M. Backes, and Y. Zhang, “Generated distributions are all you need for membership inference attacks against generative models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 4839–4849
2024
Closest in time.
J. Dubiński, A. Kowalczuk, S. Pawlak, P. Rokita, T. Trzciński, and P. Morawiecki, “Towards more realistic membership inference attacks on large diffusion models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 4860–4869
2024
Closest in time.