{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:14:30Z","timestamp":1753600470359,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819725847"},{"type":"electronic","value":"9789819725854"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-2585-4_4","type":"book-chapter","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T05:02:07Z","timestamp":1713934927000},"page":"48-62","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Deconstruction to\u00a0Reconstruction: A Plug-In Module for\u00a0Diffusion-Based Purification of\u00a0Adversarial Examples"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7273-0956","authenticated-orcid":false,"given":"Erjin","family":"Bao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7723-4591","authenticated-orcid":false,"given":"Ching-Chun","family":"Chang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2000-7977","authenticated-orcid":false,"given":"Huy H.","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4908-1860","authenticated-orcid":false,"given":"Isao","family":"Echizen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.J., Srivastava, M.B., Chang, K.W.: Generating natural language adversarial examples. In: Proceedings of Conference on Empirical Methods Natural Language Processing (EMNLP) (2018)","DOI":"10.18653\/v1\/D18-1316"},{"key":"4_CR2","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: Proceedings of International Conference on Machine Learning (ICML) (2018)"},{"key":"4_CR3","unstructured":"Brendel, W., Rauber, J., Bethge, M.: Decision-based adversarial attacks: reliable attacks against black-box machine learning models. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR4","unstructured":"Carlini, N., et al.: Hidden voice commands. In: Proceedings of USENIX Security Symposium (USENIX Security) (2016)"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: Proceedings of IEEE Symposium on Security and Privacy (SP) (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of ACM Workshop Artificial Intellgient Security (AISec) (2017)","DOI":"10.1145\/3128572.3140448"},{"key":"4_CR7","unstructured":"Croce, F., et al.: Robustbench: a standardized adversarial robustness benchmark. In: Proceedings of Advance Neural Information Processing System (NeurIPS) (2021)"},{"key":"4_CR8","unstructured":"Dhillon, G.S., et al.: Stochastic activation pruning for robust adversarial defense. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of IEEE Conference on Computer Vision on Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Efficient decision-based black-box adversarial attacks on face recognition. In: Proceedings of IEEE Conference on Computer Vision on Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00790"},{"key":"4_CR11","unstructured":"Dziugaite, G.K., Ghahramani, Z., Roy, D.M.: A study of the effect of JPG compression on adversarial images. arXiv preprint arXiv:1608.00853 (2016)"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"4_CR13","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Proceedings of International Conference on Learning Representations (ICLR) (2015)"},{"key":"4_CR14","unstructured":"Guo, C., Rana, M., Cisse, M., van\u00a0der Maaten, L.: Countering adversarial images using input transformations. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR15","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of International Conference on Learning Representations (ICLR) (2017)"},{"key":"4_CR16","unstructured":"Hendrycks, D., Gimpel, K.: Early methods for detecting adversarial images. In: Proceedings of International Conference on Learning Representations Workshop (ICLR) (2017)"},{"key":"4_CR17","unstructured":"Ilyas, A., Engstrom, L., Athalye, A., Lin, J.: Black-box adversarial attacks with limited queries and information. In: Proceedings of International Conference on Machine Learning (ICML) (2018)"},{"key":"4_CR18","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. In: Proceedings of Advance Neural Information Processing System (NeurIPS) (2019)"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: Proceedings of International Conference on Learning Representations Workshop (ICLR) (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., Zhu, J.: Defense against adversarial attacks using high-level representation guided denoiser. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00191"},{"key":"4_CR21","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arxiv:1706.06083 (2017)"},{"key":"4_CR22","unstructured":"Metzen, J.H., Genewein, T., Fischer, V., Bischoff, B.: On detecting adversarial perturbations. In: Proceedings of International Conference on Learning Representations (ICLR) (2017)"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"4_CR24","unstructured":"Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., Anandkumar, A.: Diffusion models for adversarial purification. In: Proceedings of International Conference on Machine Learning (ICML) (2022)"},{"key":"4_CR25","unstructured":"Pang, T., Xu, K., Zhu, J.: Mixup inference: better exploiting mixup to defend adversarial attacks. In: Proceedings of International Conference on Learning Representations (ICLR) (2020)"},{"key":"4_CR26","unstructured":"Raghunathan, A., Steinhardt, J., Liang, P.: Certified defenses against adversarial examples. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR27","unstructured":"Rauber, J., Brendel, W., Bethge, M.: Foolbox: a python toolbox to benchmark the robustness of machine learning models. In: Proceedings of International Conference on Machine Learning (ICML) (2017)"},{"key":"4_CR28","unstructured":"Samangouei, P., Kabkab, M., Chellappa, R.: Defense-GAN: protecting classifiers against adversarial attacks using generative models. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR29","unstructured":"Shafahi, A., et al.: Adversarial training for free! In: Proceedings of Advance Neural Information Processing System (NeurIPS) (2019)"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of ACM SIGSAC Conference on Computer Communication Security (CCS) (2016)","DOI":"10.1145\/2976749.2978392"},{"issue":"3","key":"4_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3317611","volume":"22","author":"M Sharif","year":"2019","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: A general framework for adversarial examples with objectives. ACM Trans. Priv. Secur. (TOPS) 22(3), 1\u201330 (2019)","journal-title":"ACM Trans. Priv. Secur. (TOPS)"},{"key":"4_CR32","unstructured":"Sinha, A., Namkoong, H., Duchi, J.: Certifying some distributional robustness with principled adversarial training. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR33","unstructured":"Song, Y., Kim, T., Nowozin, S., Ermon, S., Kushman, N.: Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR34","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: Proceedings of International Conference on Learning Representations (ICLR) (2014)"},{"key":"4_CR35","unstructured":"Uesato, J., O\u2019Donoghue, B., van\u00a0den Oord, A., Kohli, P.: Adversarial risk and the dangers of evaluating against weak attacks. In: Proceedings of International Conference on Machine Learning (ICML) (2018)"},{"key":"4_CR36","unstructured":"Wong, E., Kolter, Z.: Provable defenses against adversarial examples via the convex outer adversarial polytope. In: Proceedings of International Conference on Machine Learning (ICML) (2018)"},{"key":"4_CR37","unstructured":"Wong, E., Schmidt, F., Metzen, J.H., Kolter, J.Z.: Scaling provable adversarial defenses. In: Proceedings of Advance Neural Information Processing System (NeurIPS) (2018)"},{"key":"4_CR38","doi-asserted-by":"crossref","unstructured":"Xiao, C., Li, B., Zhu, J.Y., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) (2018)","DOI":"10.24963\/ijcai.2018\/543"},{"key":"4_CR39","unstructured":"Xie, C., Wang, J., Zhang, Z., Ren, Z., Yuille, A.: Mitigating adversarial effects through randomization. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"4_CR40","doi-asserted-by":"crossref","unstructured":"Xie, C., et al.: Improving transferability of adversarial examples with input diversity. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00284"},{"key":"4_CR41","doi-asserted-by":"crossref","unstructured":"Xu, W., Evans, D., Qi, Y.: Feature squeezing: detecting adversarial examples in deep neural networks. In: Proceedings of Network Distribution System on Security Symposium (NDSS) (2018)","DOI":"10.14722\/ndss.2018.23198"},{"key":"4_CR42","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30, 2805\u20132824 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"4_CR43","unstructured":"Zhang, D., Zhang, T., Lu, Y., Zhu, Z., Dong, B.: You only propagate once: accelerating adversarial training via maximal principle. In: Proceedings of Advances Neural Information Processing System (NeurIPS) (2019)"}],"container-title":["Lecture Notes in Computer Science","Digital Forensics and Watermarking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2585-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T05:02:58Z","timestamp":1713934978000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2585-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819725847","9789819725854"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2585-4_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWDW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Digital Watermarking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwdw2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwdw.site\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}