all InfoSec news
Struggle with Adversarial Defense? Try Diffusion
April 15, 2024, 4:10 a.m. | Yujie Li, Yanbin Wang, Haitao xu, Bin Liu, Jianguo Sun, Zhenhao Guo, Wenrui Ma
cs.CR updates on arXiv.org arxiv.org
Abstract: Adversarial attacks induce misclassification by introducing subtle perturbations. Recently, diffusion models are applied to the image classifiers to improve adversarial robustness through adversarial training or by purifying adversarial noise. However, diffusion-based adversarial training often encounters convergence challenges and high computational expenses. Additionally, diffusion-based purification inevitably causes data shift and is deemed susceptible to stronger adaptive attacks. To tackle these issues, we propose the Truth Maximization Diffusion Classifier (TMDC), a generative Bayesian classifier that builds upon …
adversarial adversarial attacks arxiv attacks challenges computational convergence cs.cr cs.cv data defense diffusion models expenses high image noise robustness training try
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Social Engineer For Reverse Engineering Exploit Study
@ Independent study | Remote
Security Engineer II- Full stack Java with React
@ JPMorgan Chase & Co. | Hyderabad, Telangana, India
Cybersecurity SecOps
@ GFT Technologies | Mexico City, MX, 11850
Senior Information Security Advisor
@ Sun Life | Sun Life Toronto One York
Contract Special Security Officer (CSSO) - Top Secret Clearance
@ SpaceX | Hawthorne, CA
Early Career Cyber Security Operations Center (SOC) Analyst
@ State Street | Quincy, Massachusetts