{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:03:30Z","timestamp":1743037410322,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819608393"},{"type":"electronic","value":"9789819608409"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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\/978-981-96-0840-9_6","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:28:23Z","timestamp":1734024503000},"page":"78-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ADDM: Adversarial Defenses with\u00a0Diffusion Model for\u00a0Medical Imaging Data Mining"],"prefix":"10.1007","author":[{"given":"Yimin","family":"He","sequence":"first","affiliation":[]},{"given":"Shuchao","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Anan","family":"Du","sequence":"additional","affiliation":[]},{"given":"Hechang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lele","family":"Cong","sequence":"additional","affiliation":[]},{"given":"Mehmet","family":"Orgun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Liu, J., Mian, A.: Defense against universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3389\u20133398 (2018)","DOI":"10.1109\/CVPR.2018.00357"},{"issue":"1","key":"6_CR2","first-page":"107","volume":"15","author":"R Arunkumar","year":"2018","unstructured":"Arunkumar, R., Balakrishnan, N.: Medical image classification for disease diagnosis by dbn methods. Pakistan Journal of Biotechnology 15(1), 107\u2013110 (2018)","journal-title":"Pakistan Journal of Biotechnology"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Bai, T., Luo, J., Zhao, J., Wen, B., Wang, Q.: Recent advances in adversarial training for adversarial robustness. arXiv preprint arXiv:2102.01356 (2021)","DOI":"10.24963\/ijcai.2021\/591"},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102141","volume":"73","author":"G Bortsova","year":"2021","unstructured":"Bortsova, G., Gonz\u00e1lez-Gonzalo, C., Wetstein, S.C., Dubost, F., Katramados, I., Hogeweg, L., Liefers, B., van Ginneken, B., Pluim, J.P., Veta, M., et al.: Adversarial attack vulnerability of medical image analysis systems: Unexplored factors. Med. Image Anal. 73, 102141 (2021)","journal-title":"Med. Image Anal."},{"key":"6_CR5","unstructured":"Carlini, N., Tramer, F., Dvijotham, K.D., Rice, L., Sun, M., Kolter, J.Z.: (certified!!) adversarial robustness for free! arXiv preprint arXiv:2206.10550 (2022)"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (sp). pp. 39\u201357. Ieee (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Chowdhary, K., Chowdhary, K.: Natural language processing. Fundamentals of Artificial Intelligence pp. 603\u2013649 (2020)","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Du, A., Zhou, T., Pang, S., Wu, Q., Zhang, J.: Pcl: Point contrast and labeling for weakly supervised point cloud semantic segmentation. IEEE Transactions on Multimedia pp. 1\u201312 (2024)","DOI":"10.1109\/TMM.2024.3383674"},{"key":"6_CR9","unstructured":"Gao, J., Wang, B., Lin, Z., Xu, W., Qi, Y.: Deepcloak: Masking deep neural network models for robustness against adversarial samples. arXiv preprint arXiv:1702.06763 (2017)"},{"key":"6_CR10","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"6_CR11","unstructured":"Gu, S., Rigazio, L.: Towards deep neural network architectures robust to adversarial examples. arXiv preprint arXiv:1412.5068 (2014)"},{"key":"6_CR12","unstructured":"Guo, C., Rana, M., Cisse, M., Van Der\u00a0Maaten, L.: Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017)"},{"issue":"14s","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3594869","volume":"55","author":"S Han","year":"2023","unstructured":"Han, S., Lin, C., Shen, C., Wang, Q., Guan, X.: Interpreting adversarial examples in deep learning: A review. ACM Comput. Surv. 55(14s), 1\u201338 (2023)","journal-title":"ACM Comput. Surv."},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR15","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR17","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)"},{"key":"6_CR18","unstructured":"Lee, H., Han, S., Lee, J.: Generative adversarial trainer: Defense to adversarial perturbations with gan. arXiv preprint arXiv:1705.03387 (2017)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Lu, L., Chen, Z., Lu, X., Rao, Y., Li, L., Pang, S.: Uniads: Universal architecture-distiller search for distillation gap. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a038, pp. 14167\u201314174 (2024)","DOI":"10.1609\/aaai.v38i13.29327"},{"key":"6_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332","volume":"110","author":"X Ma","year":"2021","unstructured":"Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y., Bailey, J., Lu, F.: Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recogn. 110, 107332 (2021)","journal-title":"Pattern Recogn."},{"key":"6_CR21","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"6_CR22","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162\u20138171. PMLR (2021)"},{"key":"6_CR23","unstructured":"Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., Anandkumar, A.: Diffusion models for adversarial purification. arXiv preprint arXiv:2205.07460 (2022)"},{"issue":"11","key":"6_CR24","doi-asserted-by":"publisher","first-page":"6776","DOI":"10.1109\/TCYB.2022.3195447","volume":"53","author":"S Pang","year":"2022","unstructured":"Pang, S., Du, A., Orgun, M.A., Wang, Y., Sheng, Q.Z., Wang, S., Huang, X., Yu, Z.: Beyond cnns: exploiting further inherent symmetries in medical image segmentation. IEEE Transactions on Cybernetics 53(11), 6776\u20136787 (2022)","journal-title":"IEEE Transactions on Cybernetics"},{"key":"6_CR25","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neunet.2021.03.006","volume":"140","author":"S Pang","year":"2021","unstructured":"Pang, S., Du, A., Orgun, M.A., Wang, Y., Yu, Z.: Tumor attention networks: Better feature selection, better tumor segmentation. Neural Netw. 140, 203\u2013222 (2021)","journal-title":"Neural Netw."},{"key":"6_CR26","doi-asserted-by":"publisher","first-page":"2248","DOI":"10.1007\/s00259-020-04781-3","volume":"47","author":"S Pang","year":"2020","unstructured":"Pang, S., Du, A., Orgun, M.A., Yu, Z., Wang, Y., Wang, Y., Liu, G.: Ctumorgan: a unified framework for automatic computed tomography tumor segmentation. Eur. J. Nucl. Med. Mol. Imaging 47, 2248\u20132268 (2020)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Paul, R., Schabath, M., Gillies, R., Hall, L., Goldgof, D.: Mitigating adversarial attacks on medical image understanding systems. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). pp. 1517\u20131521. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098740"},{"issue":"3","key":"6_CR28","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TC.2021.3076826","volume":"73","author":"H Qiu","year":"2021","unstructured":"Qiu, H., Zeng, Y., Zheng, Q., Guo, S., Zhang, T., Li, H.: An efficient preprocessing-based approach to mitigate advanced adversarial attacks. IEEE Trans. Comput. 73(3), 645\u2013655 (2021)","journal-title":"IEEE Trans. Comput."},{"issue":"8","key":"6_CR29","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/s10916-019-1371-9","volume":"43","author":"R Rajeev","year":"2019","unstructured":"Rajeev, R., Samath, J.A., Karthikeyan, N.: An intelligent recurrent neural network with long short-term memory (lstm) based batch normalization for medical image denoising. J. Med. Syst. 43(8), 234 (2019)","journal-title":"J. Med. Syst."},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Ross, A., Doshi-Velez, F.: Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11504"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Saeedi, S., Rezayi, S., Keshavarz, H., R.\u00a0Niakan\u00a0Kalhori, S.: Mri-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Medical Informatics and Decision Making 23(1), 16 (2023)","DOI":"10.1186\/s12911-023-02114-6"},{"issue":"5","key":"6_CR32","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828\u2013841 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"6_CR33","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"6_CR34","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.compeleceng.2018.07.042","volume":"72","author":"S Wan","year":"2018","unstructured":"Wan, S., Liang, Y., Zhang, Y.: Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers & Electrical Engineering 72, 274\u2013282 (2018)","journal-title":"Computers & Electrical Engineering"},{"key":"6_CR35","unstructured":"Wang, H., Pang, S., Lu, Z., Rao, Y., Zhou, Y., Xue, M.: dp-promise: Differentially private diffusion probabilistic models for image synthesis. In: USENIX Security (2024)"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Fu, H., Zou, W., Jia, J.: Mmcert: Provable defense against adversarial attacks to multi-modal models. arXiv preprint arXiv:2403.19080 (2024)","DOI":"10.1109\/CVPR52733.2024.02328"},{"key":"6_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101977","volume":"69","author":"M Xu","year":"2021","unstructured":"Xu, M., Zhang, T., Li, Z., Liu, M., Zhang, D.: Towards evaluating the robustness of deep diagnostic models by adversarial attack. Med. Image Anal. 69, 101977 (2021)","journal-title":"Med. Image Anal."},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Xu, W., Evans, D., Qi, Y.: Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155 (2017)","DOI":"10.14722\/ndss.2018.23198"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Zhang, R., Wu, B., Li, W., Mo, T.: Detection by attack: Detecting adversarial samples by undercover attack. In: European Symposium on Research in Computer Security. pp. 146\u2013164. Springer (2020)","DOI":"10.1007\/978-3-030-59013-0_8"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0840-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:07:53Z","timestamp":1734026873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0840-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608393","9789819608409"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0840-9_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}