all InfoSec news
Incremental Randomized Smoothing Certification
April 12, 2024, 4:11 a.m. | Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic
cs.CR updates on arXiv.org arxiv.org
Abstract: Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method constructs a smoothed DNN model and certifies its robustness through statistical sampling, but it is computationally expensive, especially when certifying with a large number of samples. Furthermore, when the smoothed model is modified (e.g., quantized or pruned), certification guarantees may not hold for the modified DNN, and recertifying from scratch can be prohibitively expensive. …
adversarial adversarial attacks arxiv attacks certificates certification cs.cr cs.lg cs.pl large networks neural networks robustness
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Social Engineer For Reverse Engineering Exploit Study
@ Independent study | Remote
DevSecOps Engineer
@ LinQuest | Beavercreek, Ohio, United States
Senior Developer, Vulnerability Collections (Contractor)
@ SecurityScorecard | Remote (Turkey or Latin America)
Cyber Security Intern 03416 NWSOL
@ North Wind Group | RICHLAND, WA
Senior Cybersecurity Process Engineer
@ Peraton | Fort Meade, MD, United States
Sr. Manager, Cybersecurity and Info Security
@ AESC | Smyrna, TN 37167, Smyrna, TN, US | Santa Clara, CA 95054, Santa Clara, CA, US | Florence, SC 29501, Florence, SC, US | Bowling Green, KY 42101, Bowling Green, KY, US