{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:39Z","timestamp":1729225719122,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Randomized smoothing is the state-of-the-art approach to constructing image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude. However, it is more complicated to compute reasonable certificates against semantic transformations (e.g., image blurring, translation, gamma correction) and their compositions. In this work, we propose General Lipschitz (GL), a new flexible framework to certify neural networks against resolvable semantic transformations. Within the framework, we analyze transformation-dependent Lipschitz-continuity of smoothed classifiers w.r.t. transformation parameters and derive corresponding robustness certificates. To assess the effectiveness of the proposed approach, we evaluate it on different image classification datasets against several state-of-the-art certification methods.<\/jats:p>","DOI":"10.3233\/faia240665","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:02:10Z","timestamp":1729170130000},"source":"Crossref","is-referenced-by-count":0,"title":["General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing"],"prefix":"10.3233","author":[{"given":"Dmitrii","family":"Korzh","sequence":"first","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Moscow, Russia"},{"name":"Artificial Intelligence Research Institute, Moscow, Russia"}]},{"given":"Mikhail","family":"Pautov","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Moscow, Russia"},{"name":"Skolkovo Institute of Science and Technology, Moscow, Russia"},{"name":"ISP RAS Research Center for Trusted Artificial Intelligence, Moscow, Russia"}]},{"given":"Olga","family":"Tsymboi","sequence":"additional","affiliation":[{"name":"Moscow Institute of Physics and Technology, Moscow, Russia"},{"name":"Sber AI Lab, Moscow, Russia"}]},{"given":"Ivan","family":"Oseledets","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Moscow, Russia"},{"name":"Skolkovo Institute of Science and Technology, Moscow, Russia"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240665","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:02:10Z","timestamp":1729170130000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240665","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}