{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T04:06:48Z","timestamp":1747454808445,"version":"3.40.5"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031923654"},{"type":"electronic","value":"9783031923661"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-92366-1_25","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T03:58:36Z","timestamp":1747367916000},"page":"322-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deceptive Diffusion: Generating Synthetic Adversarial Examples"],"prefix":"10.1007","author":[{"given":"Lucas","family":"Beerens","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2580-4115","authenticated-orcid":false,"given":"Catherine F.","family":"Higham","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6635-3461","authenticated-orcid":false,"given":"Desmond J.","family":"Higham","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,17]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"14410","DOI":"10.1109\/ACCESS.2018.2807385","volume":"6","author":"N Akhtar","year":"2018","unstructured":"Akhtar, N., Mian, A.: Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access 6, 14410\u201314430 (2018)","journal-title":"IEEE Access"},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1093\/imamat\/hxad017","volume":"89","author":"L Beerens","year":"2024","unstructured":"Beerens, L., Higham, D.J.: Adversarial ink: componentwise backward error attacks on deep learning. IMA J. Appl. Math. 89, 175\u2013196 (2024)","journal-title":"IMA J. Appl. Math."},{"key":"25_CR3","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.1109\/TKDE.2024.3361474","volume":"36","author":"H Cao","year":"2024","unstructured":"Cao, H., et al.: A survey on generative diffusion models. IEEE Trans. Knowl. Data Eng. 36, 2814\u20132830 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"25_CR4","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/TPAMI.2024.3480519","volume":"47","author":"J Chen","year":"2024","unstructured":"Chen, J., Chen, H., Chen, K., Zhang, Y., Zou, Z., Shi, Z.: Diffusion models for imperceptible and transferable adversarial attack. IEEE Trans. Pattern Anal. Mach. Intell. 47(2), 961\u2013977 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., Gao, X., Zhao, J., Ye, K., Xu, C.-Z.: AdvDiffuser: natural adversarial example synthesis with diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2023, pp.\u00a04562\u20134572 (2023)","DOI":"10.1109\/ICCV51070.2023.00421"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Chong, M.J., Forsyth, D.: Effectively unbiased FID and inception score and where to find them. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.\u00a06070\u20136079 (2020)","DOI":"10.1109\/CVPR42600.2020.00611"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Colbrook, M.J., Antun, V., Hansen, A.C.: The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smales 18th problem. Proceedings of the National Academy of Sciences (2021)","DOI":"10.1073\/pnas.2107151119"},{"key":"25_CR8","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp.\u00a08780\u20138794. Curran Associates, Inc. (2021)"},{"key":"25_CR9","unstructured":"European Comission: Proposal for a regulation of the European parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (2021)"},{"key":"25_CR10","unstructured":"European Parliament: Amendments adopted by the European parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (2023)"},{"key":"25_CR11","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/s10994-017-5663-3","volume":"107","author":"A Fawzi","year":"2018","unstructured":"Fawzi, A., Fawzi, O., Frossard, P.: Analysis of classifiers robustness to adversarial perturbations. Mach. Learn. 107, 481\u2013508 (2018)","journal-title":"Mach. Learn."},{"key":"25_CR12","unstructured":"Fowl, L., Goldblum, M., Chiang, P.-y., Geiping, J., Czaja, W., Goldstein, T.: Adversarial examples make strong poisons. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp.\u00a030339\u201330351. Curran Associates, Inc. (2021)"},{"key":"25_CR13","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, San Diego, CA (2015)"},{"key":"25_CR14","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, Long Beach, CA, USA, pp. 6626\u20136637 (2017)"},{"key":"25_CR15","unstructured":"Higham, C.F., Higham, D.J., Grindrod, P.: Diffusion models for generative artificial intelligence: an introduction for applied mathematicians, SIAM Review (to appear)"},{"key":"25_CR16","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, Red Hook, NY, USA. Curran Associates Inc. (2020)"},{"key":"25_CR17","unstructured":"Kim, H.: Torchattacks: a PyTorch repository for adversarial attacks. arXiv preprint arXiv:2010.01950 (2020)"},{"key":"25_CR18","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1038\/s41591-024-02838-6","volume":"30","author":"I Ktena","year":"2024","unstructured":"Ktena, I., et al.: Generative models improve fairness of medical classifiers under distribution shifts. Nat. Med. 30, 1166\u20131173 (2024)","journal-title":"Nat. Med."},{"key":"25_CR19","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"25_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"25_CR21","unstructured":"LeCun, Y., Cortes, C., Burges, C.J.C.: The MNIST database of handwritten digits (2010)"},{"key":"25_CR22","unstructured":"Liu, S., Wei, Y., Lu, J., Zhou, J.: An improved evaluation framework for generative adversarial networks. arXiv preprint arXiv:1803.07474 (2018)"},{"key":"25_CR23","unstructured":"Luo, C.: Understanding diffusion models: a unified perspective. arXiv:2208.11970 (2022)"},{"key":"25_CR24","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: 6th International Conference on Learning Representations, Vancouver, BC, OpenReview.net (2018)"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, NV, USA, pp.\u00a02574\u20132582. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.\u00a010684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"25_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"25_CR28","unstructured":"Shafahi, A., Huang, W., Studer, C., Feizi, S., Goldstein, T.: Are adversarial examples inevitable?. In: International Conference on Learning Representations, New Orleans, USA (2019)"},{"key":"25_CR29","unstructured":"Sutton, O.J., et al.: Stealth edits for provably fixing or attacking Large Language Models. In: Neural Information Processing Society (NeurIPS), Vancouver, Canada, December 2024"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a02818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"25_CR31","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"25_CR32","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/imamat\/hxad027","volume":"89","author":"IY Tyukin","year":"2024","unstructured":"Tyukin, I.Y., Higham, D.J., Bastounis, A., Woldegeorgis, E., Gorban, A.N.: The feasibility and inevitability of stealth attacks. IMA J. Appl. Math. 89, 44\u201384 (2024)","journal-title":"IMA J. Appl. Math."},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Tyukin, I.Y., Higham, D.J., Gorban, A.N.: On adversarial examples and stealth attacks in artificial intelligence systems. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20136. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207472"}],"container-title":["Lecture Notes in Computer Science","Scale Space and Variational Methods in Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92366-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T11:51:01Z","timestamp":1747396261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92366-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031923654","9783031923661"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92366-1_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"Code for these experiments is available from .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data Statement"}},{"value":"For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Licencing Statement"}},{"value":"SSVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Scale Space and Variational Methods in Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dartington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scalespace2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}