{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T04:15:00Z","timestamp":1775880900568,"version":"3.50.1"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Research Council","award":["101163973 (FLAIR)"],"award-info":[{"award-number":["101163973 (FLAIR)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tsp.2025.3633304","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T18:43:24Z","timestamp":1763405004000},"page":"4669-4683","source":"Crossref","is-referenced-by-count":1,"title":["Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers"],"prefix":"10.1109","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4659-3449","authenticated-orcid":false,"given":"Elad","family":"Sofer","sequence":"first","affiliation":[{"name":"School of ECE, Ben-Gurion University of the Negev, Be’er-Sheva, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2216-3369","authenticated-orcid":false,"given":"Tomer","family":"Shaked","sequence":"additional","affiliation":[{"name":"School of ECE, Ben-Gurion University of the Negev, Be’er-Sheva, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1056-673X","authenticated-orcid":false,"given":"Caroline","family":"Chaux","sequence":"additional","affiliation":[{"name":"CNRS, IPAL, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2234-929X","authenticated-orcid":false,"given":"Nir","family":"Shlezinger","sequence":"additional","affiliation":[{"name":"School of ECE, Ben-Gurion University of the Negev, Be’er-Sheva, Israel"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP55844.2023.10285957"},{"key":"ref2","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2013"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3127960"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2023.3319492"},{"key":"ref5","article-title":"Opportunities and challenges in deep learning adversarial robustness: A survey","author":"Silva","year":"2020"},{"key":"ref6","article-title":"On relating explanations and adversarial examples","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ignatiev","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01453"},{"key":"ref8","article-title":"Adversarial examples are not real features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Li","year":"2024"},{"key":"ref9","article-title":"Adversarial examples are not bugs, they are features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ilyas","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2006.879347"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3218802"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2023.3247480"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104374"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2392779"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3054167"},{"key":"ref17","first-page":"11678","article-title":"Hyperparameter tuning is all you need for LISTA","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Chen","year":"2021"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3125041"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3016905"},{"key":"ref20","article-title":"Deep unfolding: Model-based inspiration of novel deep architectures","author":"Hershey","year":"2014"},{"key":"ref21","article-title":"Unrolled Optimization with Deep Priors","author":"Diamond","year":"2017"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2021.1004075"},{"issue":"189","key":"ref23","first-page":"1","article-title":"Learning to optimize: A primer and a benchmark","volume":"23","author":"Chen","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref24","article-title":"Deep unfolding with approximated computations for rapid optimization","author":"Avrahami","year":"2025"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2007.03.003"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3178655"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177703732"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2013.09.036"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2014.12.006"},{"key":"ref32","article-title":"Efficient algorithms for smooth minimax optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Thekumparampil","year":"2019"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2948658"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref35","first-page":"953","article-title":"Towards better understanding of training certifiably robust models against adversarial examples","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Lee","year":"2021"},{"key":"ref36","first-page":"26693","article-title":"Revisiting and advancing fast adversarial training through the lens of bi-level optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang","year":"2022"},{"key":"ref37","article-title":"Optimization and optimizers for adversarial robustness","author":"Liang","year":"2023"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3648351"},{"key":"ref39","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"key":"ref41","article-title":"Nesterov accelerated gradient and scale invariance for adversarial attacks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lin","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1561\/2200000058"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1561\/2400000003"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3148324"},{"key":"ref46","first-page":"361","article-title":"Learning convex optimization control policies","volume-title":"Learning for Dynamics and Control","author":"Agrawal","year":"2020"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2023.3292472"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2475242"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20042"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref51","article-title":"Non-convex robust PCA","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Netrapalli","year":"2014"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/34.598228"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP55844.2023.10285962"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2024.3396927"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2014.2310220"},{"key":"ref56","article-title":"Visualizing the loss landscape of neural nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Li","year":"2018"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1515\/9781400833344"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/0166-218X(87)90064-3"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/78\/10807692\/11250599.pdf?arnumber=11250599","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T12:32:45Z","timestamp":1766061165000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11250599\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/tsp.2025.3633304","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"value":"1053-587X","type":"print"},{"value":"1941-0476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}