{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:09Z","timestamp":1786120809326,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":69,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"UK Research and Innovation","award":["MR\/S035176\/1"],"award-info":[{"award-number":["MR\/S035176\/1"]}]},{"name":"Engineering and Physical Sciences Research Council","award":["EP\/Z536568\/1"],"award-info":[{"award-number":["EP\/Z536568\/1"]}]},{"name":"EU Framework Program for Research and Innovation Europe Horizon","award":["101202457"],"award-info":[{"award-number":["101202457"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3817518","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:37Z","timestamp":1786036537000},"page":"10290-10301","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PRBench: A Standardized Probabilistic Robustness Benchmark"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0710-8882","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"WMG, University of Warwick, Coventry, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7146-7503","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"WMG, University of Warwick, Coventry, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1584-5708","authenticated-orcid":false,"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Liverpool, Liverpool, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-8738","authenticated-orcid":false,"given":"Wenjie","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Liverpool, Liverpool, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0974-9299","authenticated-orcid":false,"given":"Qing","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6476-2536","authenticated-orcid":false,"given":"Siddartha","family":"Khastgir","sequence":"additional","affiliation":[{"name":"WMG, University of Warwick, Coventry, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4715-212X","authenticated-orcid":false,"given":"Carsten","family":"Maple","sequence":"additional","affiliation":[{"name":"WMG, University of Warwick, Coventry, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3474-349X","authenticated-orcid":false,"given":"Xingyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"WMG, University of Warwick, Coventry, United Kingdom and School of Cyber Science and Engieering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1","article-title":"Improved generalization bounds for adversarially robust learning","volume":"23","author":"Attias Idan","year":"2022","unstructured":"Idan Attias, Aryeh Kontorovich, and Yishay Mansour. 2022. Improved generalization bounds for adversarially robust learning. Journal of Machine Learning Research, V23, 175 (2022), 1-31.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 431-441","author":"Awasthi Pranjal","year":"2020","unstructured":"Pranjal Awasthi, Natalie Frank, and Mehryar Mohri. 2020. Adversarial learning guarantees for linear hypotheses and neural networks. In International Conference on Machine Learning. PMLR, 431-441."},{"key":"e_1_3_2_1_3_1","volume-title":"8th International Conference on Learning Representations (ICLR 2020)(virtual). International Conference on Learning Representations.","author":"Balunovi\u0107 Mislav","year":"2020","unstructured":"Mislav Balunovi\u0107 and Martin Vechev. 2020. Adversarial training and provable defenses: Bridging the gap. In 8th International Conference on Learning Representations (ICLR 2020)(virtual). International Conference on Learning Representations."},{"key":"e_1_3_2_1_4_1","first-page":"312","volume-title":"ICSE'21","author":"Baluta Teodora","year":"2021","unstructured":"Teodora Baluta, Zheng Leong Chua, Kuldeep S Meel, and Prateek Saxena. 2021. Scalable quantitative verification for deep neural networks. In ICSE'21. 312-323."},{"key":"e_1_3_2_1_5_1","volume-title":"V151","author":"Chen Erh-Chung","year":"2024","unstructured":"Erh-Chung Chen and Che-Rung Lee. 2024. Data filtering for efficient adversarial training. Pattern Recognition, V151 (2024), 110394."},{"key":"e_1_3_2_1_6_1","unstructured":"Xiwei Cheng Kexin Fu and Farzan Farnia. [n.d.]. Stability and Generalization in Free Adversarial Training. Transactions on Machine Learning Research ([n.d.])."},{"key":"e_1_3_2_1_7_1","volume-title":"V142","author":"Couellan Nicolas","year":"2021","unstructured":"Nicolas Couellan. 2021. Probabilistic robustness estimates for feed-forward neural networks. Neural networks, V142 (2021), 138-147."},{"key":"e_1_3_2_1_8_1","volume-title":"35th Conf. on Neural Information Processing Systems (NeurIPS'21)","author":"Croce Francesco","year":"2021","unstructured":"Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein. 2021. Robustbench: a standardized adversarial robustness benchmark. 35th Conf. on Neural Information Processing Systems (NeurIPS'21) Track on Datasets and Benchmarks (2021)."},{"key":"e_1_3_2_1_9_1","volume-title":"Cinic-10 is not imagenet or cifar-10. arXiv preprint arXiv:1810.03505","author":"Darlow Luke N","year":"2018","unstructured":"Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey. 2018. Cinic-10 is not imagenet or cifar-10. arXiv preprint arXiv:1810.03505 (2018)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_2_1_12_1","volume-title":"AdverTorch v0.1: An Adversarial Robustness Toolbox based on PyTorch. arXiv preprint arXiv:1902.07623","author":"Ding Gavin Weiguang","year":"2019","unstructured":"Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin. 2019. AdverTorch v0.1: An Adversarial Robustness Toolbox based on PyTorch. arXiv preprint arXiv:1902.07623 (2019)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00040"},{"key":"e_1_3_2_1_14_1","volume-title":"V22, 3","author":"Dong Yi","year":"2023","unstructured":"Yi Dong, Wei Huang, Vibhav Bharti, Victoria Cox, Alec Banks, Sen Wang, Xingyu Zhao, Sven Schewe, and Xiaowei Huang. 2023. Reliability assessment and safety arguments for machine learning components in system assurance. ACM Transactions on Embedded Computing Systems, V22, 3 (2023), 1-48."},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Learning Representations.","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_16_1","unstructured":"Gintare Karolina Dziugaite Alexandre Drouin Brady Neal Nitarshan Rajkumar Ethan Caballero Linbo Wang Ioannis Mitliagkas and Daniel M Roy. 2020. In search of robust measures of generalization. Advances in Neural Information Processing Systems V33 (2020) 11723-11733."},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 3174-3185","author":"Farnia Farzan","year":"2021","unstructured":"Farzan Farnia and Asuman Ozdaglar. 2021. Train simultaneously, generalize better: Stability of gradient-based minimax learners. In International Conference on Machine Learning. PMLR, 3174-3185."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Learning Representations ICLR 2015. International Conference on Learning Representations.","author":"Goodfellow Ian J","year":"2015","unstructured":"Ian J Goodfellow. 2015. Explaining and harnessing adversarial examples. In International Conference on Learning Representations ICLR 2015. International Conference on Learning Representations."},{"key":"e_1_3_2_1_19_1","volume-title":"Advbox: a toolbox to generate adversarial examples that fool neural networks. arXiv preprint arXiv:2001.05574","author":"Goodman Dou","year":"2020","unstructured":"Dou Goodman, Hao Xin, Wang Yang, Wu Yuesheng, Xiong Junfeng, and Zhang Huan. 2020. Advbox: a toolbox to generate adversarial examples that fool neural networks. arXiv preprint arXiv:2001.05574 (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00494"},{"key":"e_1_3_2_1_21_1","volume-title":"Uncovering the limits of adversarial training against norm-bounded adversarial examples. arXiv preprint arXiv:2010.03593","author":"Gowal Sven","year":"2020","unstructured":"Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli. 2020. Uncovering the limits of adversarial training against norm-bounded adversarial examples. arXiv preprint arXiv:2010.03593 (2020)."},{"key":"e_1_3_2_1_22_1","volume-title":"V137","author":"Guo Jun","year":"2023","unstructured":"Jun Guo, Wei Bao, Jiakai Wang, Yuqing Ma, Xinghai Gao, Gang Xiao, Aishan Liu, Jian Dong, Xianglong Liu, and Wenjun Wu. 2023. A comprehensive evaluation framework for deep model robustness. Pattern Recognition, V137 (2023), 109308."},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR, 1225-1234","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Ben Recht, and Yoram Singer. 2016. Train faster, generalize better: Stability of stochastic gradient descent. In International conference on machine learning. PMLR, 1225-1234."},{"key":"e_1_3_2_1_24_1","volume-title":"SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. In IEEE\/CVF International Conference on Computer Vision (ICCV'23)","author":"Huang Wei","year":"2023","unstructured":"Wei Huang, Xingyu Zhao, Gaojie Jin, and Xiaowei Huang. 2023. SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. In IEEE\/CVF International Conference on Computer Vision (ICCV'23). 1988-1998."},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations.","author":"Jiang Yiding","year":"2020","unstructured":"Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio. 2020. Fantastic Generalization Measures and Where to Find Them. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_26_1","volume-title":"Adversarial logit pairing. arXiv preprint arXiv:1803.06373","author":"Kannan Harini","year":"2018","unstructured":"Harini Kannan, Alexey Kurakin, and Ian Goodfellow. 2018. Adversarial logit pairing. arXiv preprint arXiv:1803.06373 (2018)."},{"key":"e_1_3_2_1_27_1","volume-title":"Adversarial risk bounds via function transformation. arXiv preprint arXiv:1810.09519","author":"Khim Justin","year":"2018","unstructured":"Justin Khim and Po-Ling Loh. 2018. Adversarial risk bounds via function transformation. arXiv preprint arXiv:1810.09519 (2018)."},{"key":"e_1_3_2_1_28_1","volume-title":"V36","author":"Kim Hoki","year":"2023","unstructured":"Hoki Kim, Jinseong Park, Yujin Choi, and Jaewook Lee. 2023. Fantastic robustness measures: The secrets of robust generalization. Advances in Neural Information Processing Systems, V36 (2023), 48793-48818."},{"key":"e_1_3_2_1_29_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_30_1","volume-title":"V7, 7","author":"Le Yann","year":"2015","unstructured":"Yann Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. CS 231N, V7, 7 (2015), 3."},{"key":"e_1_3_2_1_31_1","volume-title":"Deeprobust: A pytorch library for adversarial attacks and defenses. arXiv preprint arXiv:2005.06149","author":"Li Yaxin","year":"2020","unstructured":"Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang. 2020. Deeprobust: A pytorch library for adversarial attacks and defenses. arXiv preprint arXiv:2005.06149 (2020)."},{"key":"e_1_3_2_1_32_1","volume-title":"V37","author":"Li Zaitang","year":"2024","unstructured":"Zaitang Li, Pin-Yu Chen, and Tsung-Yi Ho. 2024. GREAT score: Global robustness evaluation of adversarial perturbation using generative models. Advances in Neural Information Processing Systems, V37 (2024), 39158-39189."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00023"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02196-3"},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Learning Representations.","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_36_1","volume-title":"V6","author":"Melis Marco","year":"2019","unstructured":"Marco Melis, Ambra Demontis, Maura Pintor, Angelo Sotgiu, and Battista Biggio. 2019. secml: A python library for secure and explainable machine learning. arXiv preprint arXiv:1912.10013, V6 (2019)."},{"key":"e_1_3_2_1_37_1","volume-title":"Conference on Learning Theory. PMLR, 2512-2530","author":"Montasser Omar","year":"2019","unstructured":"Omar Montasser, Steve Hanneke, and Nathan Srebro. 2019. Vc classes are adversarially robustly learnable, but only improperly. In Conference on Learning Theory. PMLR, 2512-2530."},{"key":"e_1_3_2_1_38_1","volume-title":"NIPS workshop on deep learning and unsupervised feature learning","volume":"2011","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al., 2011. Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, Vol. V2011. Granada, 4."},{"key":"e_1_3_2_1_39_1","volume-title":"Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, et al.","author":"Nicolae Maria-Irina","year":"2018","unstructured":"Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, et al., 2018. Adversarial Robustness Toolbox v0. 4.0. arXiv preprint arXiv:1807.01069 (2018)."},{"key":"e_1_3_2_1_40_1","unstructured":"Nicolas Papernot Fartash Faghri Nicholas Carlini Ian Goodfellow Reuben Feinman Alexey Kurakin Cihang Xie Yash Sharma Tom Brown Aurko Roy et al. 2016a. Technical report on the cleverhans v2. 1.0 adversarial examples library. arXiv preprint arXiv:1610.00768 (2016)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20768"},{"key":"e_1_3_2_1_43_1","volume-title":"Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models. CoRR","author":"Rauber Jonas","year":"2017","unstructured":"Jonas Rauber, Wieland Brendel, and Matthias Bethge. 2017. Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models. CoRR (2017)."},{"key":"e_1_3_2_1_44_1","volume-title":"International conference on machine learning. PMLR, 8093-8104","author":"Rice Leslie","year":"2020","unstructured":"Leslie Rice, Eric Wong, and Zico Kolter. 2020. Overfitting in adversarially robust deep learning. In International conference on machine learning. PMLR, 8093-8104."},{"key":"e_1_3_2_1_45_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Robey Alexander","year":"2022","unstructured":"Alexander Robey, Luiz Chamon, George J Pappas, and Hamed Hassani. 2022. Probabilistically robust learning: Balancing average and worst-case performance. In International Conference on Machine Learning. PMLR, 18667-18686."},{"key":"e_1_3_2_1_46_1","volume-title":"In Proc. of 2nd Int. Conf. on Learning Representations.","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In In Proc. of 2nd Int. Conf. on Learning Representations."},{"key":"e_1_3_2_1_47_1","volume-title":"Robustart: Benchmarking robustness on architecture design and training techniques. arXiv preprint arXiv:2109.05211","author":"Tang Shiyu","year":"2021","unstructured":"Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, et al., 2021. Robustart: Benchmarking robustness on architecture design and training techniques. arXiv preprint arXiv:2109.05211 (2021)."},{"key":"e_1_3_2_1_48_1","volume-title":"V34","author":"Tit Karim","year":"2021","unstructured":"Karim Tit, Teddy Furon, and Mathias Rousset. 2021. Efficient statistical assessment of neural network corruption robustness. NeurIPS'21, V34 (2021), 9253-9263."},{"key":"e_1_3_2_1_49_1","first-page":"323","volume-title":"Proc. of The 26th Int. Conf. on Artificial Intelligence and Statistics","volume":"206","author":"Teddy Furon Karim TIT","year":"2023","unstructured":"Karim TIT, Teddy Furon, and Mathias Rousset. 2023. Gradient-Informed Neural Network Statistical Robustness Estimation. In Proc. of The 26th Int. Conf. on Artificial Intelligence and Statistics, Vol. V206. PMLR, 323-334."},{"key":"e_1_3_2_1_50_1","volume-title":"International conference on machine learning. PMLR, 10347-10357","author":"Touvron Hugo","year":"2021","unstructured":"Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herv\u00e9 J\u00e9gou. 2021. Training data-efficient image transformers & distillation through attention. In International conference on machine learning. PMLR, 10347-10357."},{"key":"e_1_3_2_1_51_1","volume-title":"Ensemble Adversarial Training: Attacks and Defenses. In International Conference on Learning Representations.","author":"Tram\u00e8r Florian","year":"2018","unstructured":"Florian Tram\u00e8r, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2018. Ensemble Adversarial Training: Attacks and Defenses. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_52_1","first-page":"1735","article-title":"Statistically robust neural network classification. In Uncertainty in Artificial Intelligence","author":"Wang Benjie","year":"2021","unstructured":"Benjie Wang, Stefan Webb, and Tom Rainforth. 2021. Statistically robust neural network classification. In Uncertainty in Artificial Intelligence. PMLR, 1735-1745.","journal-title":"PMLR"},{"key":"e_1_3_2_1_53_1","volume-title":"International conference on learning representations.","author":"Wang Yisen","year":"2019","unstructured":"Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu. 2019. Improving adversarial robustness requires revisiting misclassified examples. In International conference on learning representations."},{"key":"e_1_3_2_1_54_1","volume-title":"Int. Conf. on Learning Representations.","author":"Webb Stefan","year":"2019","unstructured":"Stefan Webb, Tom Rainforth, Yee Whye Teh, and M Pawan Kumar. 2019. A statistical approach to assessing neural network robustness. In Int. Conf. on Learning Representations."},{"key":"e_1_3_2_1_55_1","volume-title":"Int. Conf. on Machine Learning. PMLR, 6727-6736","author":"Weng Lily","year":"2019","unstructured":"Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy, Ivan Oseledets, and Luca Daniel. 2019. PROVEN: Verifying robustness of neural networks with a probabilistic approach. In Int. Conf. on Machine Learning. PMLR, 6727-6736."},{"key":"e_1_3_2_1_56_1","volume-title":"Adversarial rademacher complexity of deep neural networks. arXiv preprint arXiv:2211.14966","author":"Xiao Jiancong","year":"2022","unstructured":"Jiancong Xiao, Yanbo Fan, Ruoyu Sun, and Zhi-Quan Luo. 2022a. Adversarial rademacher complexity of deep neural networks. arXiv preprint arXiv:2211.14966 (2022)."},{"key":"e_1_3_2_1_57_1","volume-title":"V35","author":"Xiao Jiancong","year":"2022","unstructured":"Jiancong Xiao, Yanbo Fan, Ruoyu Sun, Jue Wang, and Zhi-Quan Luo. 2022b. Stability analysis and generalization bounds of adversarial training. Advances in Neural Information Processing Systems, V35 (2022), 15446-15459."},{"key":"e_1_3_2_1_58_1","volume-title":"Adaptive smoothness-weighted adversarial training for multiple perturbations with its stability analysis. arXiv preprint arXiv:2210.00557","author":"Xiao Jiancong","year":"2022","unstructured":"Jiancong Xiao, Zeyu Qin, Yanbo Fan, Baoyuan Wu, Jue Wang, and Zhi-Quan Luo. 2022c. Adaptive smoothness-weighted adversarial training for multiple perturbations with its stability analysis. arXiv preprint arXiv:2210.00557 (2022)."},{"key":"e_1_3_2_1_59_1","volume-title":"V34","author":"Xing Yue","year":"2021","unstructured":"Yue Xing, Qifan Song, and Guang Cheng. 2021. On the algorithmic stability of adversarial training. Advances in neural information processing systems, V34 (2021), 26523-26535."},{"key":"e_1_3_2_1_60_1","volume-title":"International conference on machine learning. PMLR, 7085-7094","author":"Yin Dong","year":"2019","unstructured":"Dong Yin, Ramchandran Kannan, and Peter Bartlett. 2019. Rademacher complexity for adversarially robust generalization. In International conference on machine learning. PMLR, 7085-7094."},{"key":"e_1_3_2_1_61_1","volume-title":"International conference on machine learning. PMLR, 7472-7482","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning. PMLR, 7472-7482."},{"key":"e_1_3_2_1_62_1","unstructured":"Ruihan Zhang and Jun Sun. 2025. Are Probabilistic Robust Accuracy Bounded. https:\/\/openreview.net\/forum?id=fVgUXaesSS"},{"key":"e_1_3_2_1_63_1","volume-title":"Towards Certified Probabilistic Robustness with High Accuracy. arXiv preprint arXiv:2309.00879","author":"Zhang Ruihan","year":"2023","unstructured":"Ruihan Zhang, Peixin Zhang, and Jun Sun. 2023b. Towards Certified Probabilistic Robustness with High Accuracy. arXiv preprint arXiv:2309.00879 (2023)."},{"key":"e_1_3_2_1_64_1","volume-title":"Fieldsend","author":"Zhang Tianle","year":"2023","unstructured":"Tianle Zhang, Wenjie Ruan, and Jonathan E. Fieldsend. 2023a. PRoA: A Probabilistic Robustness Assessment Against Functional Perturbations. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19\u201323, 2022, Proceedings, Part III. Springer-Verlag, 154\u2013170."},{"key":"e_1_3_2_1_65_1","volume-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 559-567","author":"Zhang Tianle","year":"2024","unstructured":"Tianle Zhang, Yanghao Zhang, Ronghui Mu, Jiaxu Liu, Jonathan Fieldsend, and Wenjie Ruan. 2024b. PRASS: probabilistic risk-averse robust learning with stochastic search. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 559-567."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51701.2025.00164"},{"key":"e_1_3_2_1_67_1","volume-title":"European Conference on Computer Vision.","author":"Zhang Yi","year":"2024","unstructured":"Yi Zhang, Yun Tang, Wenjie Ruan, Xiaowei Huang, Siddartha Khastgir, Paul Jennings, and Xingyu Zhao. 2024a. ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation. In European Conference on Computer Vision."},{"key":"e_1_3_2_1_68_1","volume-title":"Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide. Adversarial Example Detection and Mitigation Using Machine Learning","author":"Zhao Xingyu","year":"2025","unstructured":"Xingyu Zhao. 2025. Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide. Adversarial Example Detection and Mitigation Using Machine Learning (2025), 1-13."},{"key":"e_1_3_2_1_69_1","unstructured":"Xingyu Zhao Wei Huang Alec Banks Victoria Cox David Flynn Sven Schewe and Xiaowei Huang. 2021. Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles. In AISafety'21."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:17:24Z","timestamp":1786065444000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3817518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":69,"alternative-id":["10.1145\/3770855.3817518","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3817518","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}