{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T20:01:19Z","timestamp":1785355279799,"version":"3.55.0"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1109\/icsccc69031.2026.11600379","type":"proceedings-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:45:43Z","timestamp":1784238343000},"page":"1007-1012","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Backdoored Models using Gradient Entropy Analysis of Adversarial Inputs"],"prefix":"10.1109","author":[{"given":"Ishan","family":"Panwar","sequence":"first","affiliation":[{"name":"Delhi Technological University","place":["Delhi, India"],"department":["Department of Computer Science & Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anukriti","family":"Kaushal","sequence":"additional","affiliation":[{"name":"Delhi Technological University","place":["Delhi, India"],"department":["Department of Computer Science & Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Jain","sequence":"additional","affiliation":[{"name":"Shri Ram Murti Smarak College of Engineering & Technology","department":["Department of Computer Science & Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeev","family":"Kumar","sequence":"additional","affiliation":[{"name":"Delhi Technological University","place":["Delhi, India"],"department":["Department of Computer Science & Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Altaf","family":"Husain","sequence":"additional","affiliation":[{"name":"CommScope \/ Ruckus Networks","place":["Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitendra","family":"Singh","sequence":"additional","affiliation":[{"name":"CERT-In, Ministry of Electronics and Information Technology","place":["Delhi, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Learning multiple layers of features from tiny images","volume-title":"University of Toronto, Tech. Rep.","author":"Krizhevsky","year":"2009"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref3","article-title":"Intriguing properties of neural networks","volume-title":"2nd International Conference on Learning Representations (ICLR 2014)","author":"Szegedy"},{"key":"ref4","article-title":"Explaining and harnessing adversarial examples","volume-title":"International Conference on Learning Representations (ICLR)","author":"Goodfellow"},{"key":"ref5","article-title":"Adversarial examples are not bugs, they are features","author":"Ilyas","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref6","article-title":"Towards deep learning models resistant to adversarial attacks","volume-title":"6th International Conference on Learning Representations (ICLR 2018)","author":"Madry"},{"key":"ref7","first-page":"1","article-title":"Badnets: Identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017","journal-title":"IEEE Access"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.14722\/ndss.2018.23291","article-title":"Trojaning attack on neural networks","volume-title":"Proceedings of the Network and Distributed System Security Symposium (NDSS)","author":"Liu"},{"key":"ref9","article-title":"Targeted backdoor attacks on deep learning systems using data poisoning","author":"Chen","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417253"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2017.16"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/icip.2019.8802997"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_11"},{"key":"ref14","article-title":"WaNet \u2013 imperceptible warping-based backdoor attack","volume-title":"9th International Conference on Learning Representations (ICLR 2021)","author":"Nguyen"},{"key":"ref15","first-page":"3454","article-title":"Input-aware dynamic backdoor attack","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Nguyen"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/sp.2019.00031"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363216"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00038"},{"key":"ref19","article-title":"Detecting backdoor attacks on deep neural networks by activation clustering","volume-title":"Workshop on Artificial Intelligence Safety 2019 (SafeAI) colocated with AAAI 2019","volume":"2301","author":"Chen"},{"key":"ref20","article-title":"Spectre: Defending against backdoor attacks using robust statistics","volume-title":"International Conference on Machine Learning (ICML)","author":"Hayase"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"ref22","article-title":"Adversarial neuron pruning purifies backdoored deep models","author":"Wu","year":"2021","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref23","article-title":"Neural attention distillation: Erasing backdoor triggers from deep neural networks","volume-title":"9th International Conference on Learning Representations (ICLR 2021)","author":"Li"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01963"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3392760"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359790"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417231"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/isdfs69419.2026.11459068"},{"key":"ref29","first-page":"8011","article-title":"Spectral signatures in backdoor attacks","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems (NeurIPS)","author":"Tran"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66415-2_4"}],"event":{"name":"2026 Fourth International Conference on Secure Cyber Computing and Communications (ICSCCC)","location":"Jalandhar, India","start":{"date-parts":[[2026,5,29]]},"end":{"date-parts":[[2026,5,31]]}},"container-title":["2026 Fourth International Conference on Secure Cyber Computing and Communications (ICSCCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11599967\/11599968\/11600379.pdf?arnumber=11600379","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T19:13:12Z","timestamp":1785352392000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11600379\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/icsccc69031.2026.11600379","relation":{},"subject":[],"published":{"date-parts":[[2026,5]]}}}