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Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, \u201cDeep unsupervised learning using nonequilibrium thermodynamics,\u201d Proc. ICML 2015, PMLR, vol.37, pp.2256-2265, PMLR, 2015."},{"key":"2","unstructured":"[2] J. Ho, A. Jain, and P. Abbeel, \u201cDenoising diffusion probabilistic models,\u201d Proc. NeurIPS 2020, pp.6840-6851, Curran Associates, Inc., 2020."},{"key":"3","unstructured":"[3] P. Dhariwal and A. Nichol, \u201cDiffusion models beat gans on image synthesis,\u201d Advances in Neural Information Processing Systems, vol.34, pp.8780-8794, 2021."},{"key":"4","unstructured":"[4] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, \u201cHierarchical text-conditional image generation with clip latents,\u201d arXiv preprint arXiv:2204.06125, 2022."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, \u201cHigh-resolution image synthesis with latent diffusion models,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.10684-10695, 2022. 10.1109\/cvpr52688.2022.01042","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S.K.S. Ghasemipour, B.K. Ayan, S.S. Mahdavi, R.G. Lopes, et al., \u201cPhotorealistic text-to-image diffusion models with deep language understanding,\u201d arXiv preprint arXiv:2205.11487, 2022.","DOI":"10.1145\/3528233.3530757"},{"key":"7","unstructured":"[7] A.Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, \u201cGLIDE: Towards photorealistic image generation and editing with text-guided diffusion models,\u201d Proceedings of the 39th International Conference on Machine Learning, pp.16784-16804, 2022."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] H. Hu and J. Pang, \u201cLoss and likelihood based membership inference of diffusion models,\u201d Proceedings of the 26th Information Security Conference (ISC\u201923), Springer Nature Switzerland, 2023. 10.1007\/978-3-031-49187-0_7","DOI":"10.1007\/978-3-031-49187-0_7"},{"key":"9","unstructured":"[9] N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tram\u00e8r, B. Balle, D. Ippolito, and E. Wallace, \u201cExtracting training data from diffusion models,\u201d 2023 USENIX Security Symposium (USENIX Security), pp.5253-5270, USENIX Association, 2023."},{"key":"10","unstructured":"[10] J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu, \u201cAre diffusion models vulnerable to membership inference attacks?,\u201d Proceedings of the 40th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol.202, pp.8717-8730, PMLR, 2023."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] X. Yi, E. Walia, and P. Babyn, \u201cGenerative adversarial network in medical imaging: A review,\u201d Medical Image Analysis, vol.58, p.101552, 2019. 10.1016\/j.media.2019.101552","DOI":"10.1016\/j.media.2019.101552"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] A. Kazerouni, E.K. Aghdam, M. Heidari, R. Azad, M. Fayyaz, I. Hacihaliloglu, and D. Merhof, \u201cDiffusion models in medical imaging: A comprehensive survey,\u201d Medical Image Analysis, vol.88, p.102846, 2023. 10.1016\/j.media.2023.102846","DOI":"10.1016\/j.media.2023.102846"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] B. Hilprecht, M. H\u00e4rterich, and D. Bernau, \u201cMonte carlo and reconstruction membership inference attacks against generative models,\u201d Proceedings of Privacy Enhancing Technologies, vol.2019, no.4, pp.232-249, 2019. 10.2478\/popets-2019-0067","DOI":"10.2478\/popets-2019-0067"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] R. Shokri, M. Stronati, C. Song, and V. Shmatikov, \u201cMembership inference attacks against machine learning models,\u201d 2017 IEEE symposium on security and privacy (SP), pp.3-18, IEEE, 2017. 10.1109\/sp.2017.41","DOI":"10.1109\/SP.2017.41"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] M. Conti, J. Li, S. Picek, and J. Xu, \u201cLabel-only membership inference attack against node-level graph neural networks,\u201d Proc. AISec 2022, pp.1-12, 2022. 10.1145\/3560830.3563734","DOI":"10.1145\/3560830.3563734"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Z. Li, Y. Liu, X. He, N. Yu, M. Backes, and Y. Zhang, \u201cAuditing membership leakages of multi-exit networks,\u201d Proc. CCS 2022, pp.1917-1931, ACM, 2022. 10.1145\/3548606.3559359","DOI":"10.1145\/3548606.3559359"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Ye, A. Maddi, S.K. Murakonda, V. Bindschaedler, and R. Shokri, \u201cEnhanced membership inference attacks against machine learning models,\u201d Proc. CCS 2022, pp.3093-3106, ACM, 2022. 10.1145\/3548606.3560675","DOI":"10.1145\/3548606.3560675"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] J. Hayes, L. Melis, G. Danezis, and E.D. Cristofaro, \u201cLOGAN: membership inference attacks against generative models,\u201d Proceedings of Privacy Enhancing Technologies, vol.2019, no.1, pp.133-152, 2019. 10.2478\/popets-2019-0008","DOI":"10.2478\/popets-2019-0008"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] S. Mukherjee, Y. Xu, A. Trivedi, N. Patowary, and J.L. Ferres, \u201cprivgan: Protecting gans from membership inference attacks at low cost to utility.,\u201d Proceedings of Privacy Enhancing Technologies, vol.2021, no.3, pp.142-163, 2021. 10.2478\/popets-2021-0041","DOI":"10.2478\/popets-2021-0041"},{"key":"20","unstructured":"[20] T. Stadler, B. Oprisanu, and C. Troncoso, \u201cSynthetic data-anonymisation groundhog day,\u201d Proc. USENIX Security 2022, pp.1451-1468, 2022."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] T. Matsumoto, T. Miura, and N. Yanai, \u201cMembership inference attacks against diffusion models,\u201d Proc. IEEE SPW 2023, pp.77-83, IEEE, 2023. 10.1109\/spw59333.2023.00013","DOI":"10.1109\/SPW59333.2023.00013"},{"key":"22","unstructured":"[22] J. Song, C. Meng, and S. Ermon, \u201cDenoising diffusion implicit models,\u201d CoRR, abs\/2010.02502, 2020."},{"key":"23","unstructured":"[23] A. Radford, L. Metz, and S. Chintala, \u201cUnsupervised representation learning with deep convolutional generative adversarial networks,\u201d 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, ed. Y. Bengio and Y. LeCun, 2016."},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes, \u201cMl-leaks: Model and data independent membership inference attacks and defenses on machine learning models,\u201d Proc. NDSS 2019, The Internet Society, 2019. 10.14722\/ndss.2019.23119","DOI":"10.14722\/ndss.2019.23119"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tram\u00e8r, \u201cMembership inference attacks from first principles,\u201d Proc. IEEE S&P 2022, pp.1897-1914, IEEE, 2022. 10.1109\/sp46214.2022.9833649","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] L. Song, R. Shokri, and P. Mittal, \u201cMembership inference attacks against adversarially robust deep learning models,\u201d Proc. IEEE SPW 2019, pp.50-56, IEEE, 2019. 10.1109\/spw.2019.00021","DOI":"10.1109\/SPW.2019.00021"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] F. Tram\u00e8r, R. Shokri, A. San Joaquin, H. Le, M. Jagielski, S. Hong, and N. Carlini, \u201cTruth serum: Poisoning machine learning models to reveal their secrets,\u201d Proc. CCS 2022, pp.2779-2792, ACM, 2022. 10.1145\/3548606.3560554","DOI":"10.1145\/3548606.3560554"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] S. Hidano, T. Murakami, S. Katsumata, S. Kiyomoto, and G. Hnnaoka, \u201cModel inversion attacks for online prediction systems: Without knowledge of non-sensitive attributes,\u201d IEICE Transactions on Information and Systems, vol.E101-D, no.11, pp.2665-2676, 2018. 10.1587\/transinf.2017icp0013","DOI":"10.1587\/transinf.2017ICP0013"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] Z. Wang, Y. Huang, M. Song, L. Wu, F. Xue, and K. Ren, \u201cPoisoning-assisted property inference attack against federated learning,\u201d IEEE Transactions on Dependable and Secure Computing, vol.20, no.4, pp.3328-3340, 2022. 10.1109\/tdsc.2022.3196646","DOI":"10.1109\/TDSC.2022.3196646"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] S. Mahloujifar, E. Ghosh, and M. Chase, \u201cProperty inference from poisoning,\u201d Proc. IEEE S&P 2022, pp.1120-1137, IEEE, 2022. 10.1109\/sp46214.2022.9833623","DOI":"10.1109\/SP46214.2022.9833623"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] C. Dwork, \u201cDifferential privacy,\u201d Proc. ICALP, LNCS, vol.4052, pp.1-12, Springer, 2006. 10.1007\/11787006_1","DOI":"10.1007\/11787006_1"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, and L. Zhang, \u201cDeep learning with differential privacy,\u201d Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp.308-318, 2016. 10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, \u201cPrivacy risk in machine learning: Analyzing the connection to overfitting,\u201d Proc. CSF 2018, pp.268-282, IEEE, 2018. 10.1109\/csf.2018.00027","DOI":"10.1109\/CSF.2018.00027"},{"key":"34","unstructured":"[34] B. Jayaraman and D. Evans, \u201cEvaluating differentially private machine learning in practice,\u201d Proc. USENIX Security 2019, pp.1895-1912, USENIX Association, 2019."},{"key":"35","unstructured":"[35] M. Jagielski, J. Ullman, and A. Oprea, \u201cAuditing differentially private machine learning: How private is private sgd?,\u201d Advances in Neural Information Processing Systems, pp.22205-22216, Curran Associates, Inc., 2020."},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] D. Chen, N. Yu, Y. Zhang, and M. Fritz, \u201cGAN-leaks: A taxonomy of membership inference attacks against generative models,\u201d Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, ACM, pp.343-362, 2020. 10.1145\/3372297.3417238","DOI":"10.1145\/3372297.3417238"},{"key":"37","unstructured":"[37] A. Krizhevsky, G. Hinton, et al., \u201cLearning multiple layers of features from tiny images,\u201d 2009."},{"key":"38","unstructured":"[38] Z. Liu, P. Luo, X. Wang, and X. Tang, \u201cDeep learning face attributes in the wild,\u201d CoRR, abs\/1411.7766, 2014."},{"key":"39","unstructured":"[39] Y. Wu, N. Yu, Z. Li, M. Backes, and Y. Zhang, \u201cMembership inference attacks against text-to-image generation models,\u201d CoRR, abs\/2210.00968, 2022."},{"key":"40","unstructured":"[40] C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al., \u201cLaion-5b: An open large-scale dataset for training next generation image-text models,\u201d Advances in Neural Information Processing Systems, vol.35, pp.25278-25294, 2022."},{"key":"41","unstructured":"[41] Y. Song and S. Ermon, \u201cGenerative modeling by estimating gradients of the data distribution,\u201d Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019."},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] T. Karras, S. Laine, and T. Aila, \u201cA style-based generator architecture for generative adversarial networks,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 10.1109\/cvpr.2019.00453","DOI":"10.1109\/CVPR.2019.00453"},{"key":"43","unstructured":"[43] \u201cKaggle.com: Diabetic retinopathy detection.\u201d https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection#references, 2015."},{"key":"44","unstructured":"[44] Y. Song, J. Sohl-Dickstein, D.P. Kingma, A. Kumar, S. Ermon, and B. Poole, \u201cScore-based generative modeling through stochastic differential equations,\u201d International Conference on Learning Representations, 2021."},{"key":"45","unstructured":"[45] A. Nichol and P. Dhariwal, \u201cImproved denoising diffusion probabilistic models,\u201d CoRR, abs\/2102.09672, 2021."},{"key":"46","unstructured":"[46] A. Coates, A. Ng, and H. Lee, \u201cAn analysis of single-layer networks in unsupervised feature learning,\u201d Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp.215-223, JMLR Workshop and Conference Proceedings, 2011."},{"key":"47","unstructured":"[47] J.N.M. Pinkney, \u201cPokemon blip captions.\u201d https:\/\/huggingface.co\/datasets\/lambdalabs\/pokemon-blip-captions\/, 2022."},{"key":"48","doi-asserted-by":"crossref","unstructured":"[48] T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll\u00e1r, and C.L. Zitnick, \u201cMicrosoft coco: Common objects in context,\u201d Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp.740-755, Springer, 2014. 10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"49","unstructured":"[49] D. Zhu, D. Chen, J. Grossklags, and M. Fritz, \u201cData forensics in diffusion models: A systematic analysis of membership privacy,\u201d CoRR, abs\/2302.07801, 2023."},{"key":"50","unstructured":"[50] F. Kong, J. Duan, R. Ma, H.T. Shen, X. Zhu, X. Shi, and K. Xu, \u201cAn efficient membership inference attack for the diffusion model by proximal initialization,\u201d The Twelfth International Conference on Learning Representations, 2024."},{"key":"51","unstructured":"[51] V. Popov, I. Vovk, V. Gogoryan, T. Sadekova, and M. Kudinov, \u201cGrad-tts: A diffusion probabilistic model for text-to-speech,\u201d International Conference on Machine Learning, pp.8599-8608, PMLR, 2021."},{"key":"52","doi-asserted-by":"crossref","unstructured":"[52] R. Huang, M.W.Y. Lam, J. Wang, D. Su, D. Yu, Y. Ren, and Z. Zhao, \u201cFastdiff: A fast conditional diffusion model for high-quality speech synthesis,\u201d Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pp.4157-4163, 2022. 10.24963\/ijcai.2022\/577","DOI":"10.24963\/ijcai.2022\/577"},{"key":"53","unstructured":"[53] K. Ito and L. Johnson, \u201cThe lj speech dataset.\u201d https:\/\/keithito.com\/LJ-Speech-Dataset\/, 2017."},{"key":"54","unstructured":"[54] Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, \u201cDiffwave: A versatile diffusion model for audio synthesis,\u201d International Conference on Learning Representations, 2021."},{"key":"55","unstructured":"[55] V.C. Yamagishi Junichi and M. Kirsten, \u201cCstr vctk corpus: English multi-speaker corpus for cstr voice cloning.\u201d https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443, 2019."},{"key":"56","doi-asserted-by":"crossref","unstructured":"[56] H. Zen, V. Dang, R. Clark, Y. Zhang, R.J. Weiss, Y. Jia, Z. Chen, and Y. Wu, \u201cLibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech,\u201d Proc. Interspeech 2019, pp.1526-1530, 2019. 10.21437\/interspeech.2019-2441","DOI":"10.21437\/Interspeech.2019-2441"},{"key":"57","doi-asserted-by":"crossref","unstructured":"[57] J. Dubi\u0144ski, A. Kowalczuk, S. Pawlak, P. Rokita, T. Trzcinski, and P. Morawiecki, \u201cTowards more realistic membership inference attacks on large diffusion models,\u201d Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp.4860-4869, Jan. 2024. 10.1109\/wacv57701.2024.00479","DOI":"10.1109\/WACV57701.2024.00479"},{"key":"58","unstructured":"[58] Y. Pang, T. Wang, X. Kang, M. Huai, and Y. Zhang, \u201cWhite-box membership inference attacks against diffusion models,\u201d arXiv preprint arXiv:2308.06405, 2023."},{"key":"59","doi-asserted-by":"crossref","unstructured":"[59] M. Zhang, N. Yu, R. Wen, M. Backes, and Y. Zhang, \u201cGenerated distributions are all you need for membership inference attacks against generative models,\u201d Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp.4839-4849, Jan. 2024. 10.1109\/wacv57701.2024.00477","DOI":"10.1109\/WACV57701.2024.00477"},{"key":"60","unstructured":"[60] Z. Kong and W. Ping, \u201cOn fast sampling of diffusion probabilistic models,\u201d ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 2021."},{"key":"61","unstructured":"[61] F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao, \u201cLSUN: construction of a large-scale image dataset using deep learning with humans in the loop,\u201d CoRR, abs\/1506.03365, 2015."},{"key":"62","unstructured":"[62] T. Cilloni, C. Fleming, and C. Walter, \u201cPrivacy threats in stable diffusion models,\u201d arXiv preprint arXiv:2311.09355, 2023."},{"key":"63","unstructured":"[63] S. Wu, S. Tang, S. Aydore, M. Kearns, and A. Roth, \u201cMembership inference attack on diffusion models via quantile regression,\u201d NeurIPS 2023 Workshop on Regulatable ML, 2023."},{"key":"64","unstructured":"[64] X. Fu, X. Wang, Q. Li, J. Liu, J. Dai, and J. Han, \u201cModel will tell: Training membership inference for diffusion models,\u201d arXiv preprint arXiv:2403.08487, 2024."},{"key":"65","doi-asserted-by":"crossref","unstructured":"[65] C. Song and V. Shmatikov, \u201cAuditing data provenance in text-generation models,\u201d Proc. CCS 2019, pp.196-206, ACM, 2019. 10.1145\/3292500.3330885","DOI":"10.1145\/3292500.3330885"},{"key":"66","unstructured":"[66] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, G. Klambauer, and S. Hochreiter, \u201cGans trained by a two time-scale update rule converge to a nash equilibrium,\u201d CoRR, abs\/1706.08500, 2017."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/12\/E108.D_2024ICP0005\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T03:27:48Z","timestamp":1764991668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/12\/E108.D_2024ICP0005\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":66,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024icp0005","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,12,1]]},"article-number":"2024ICP0005"}}