arXiv (All)
open access
Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
Consensus-based optimization (CBO) has established itself as an efficient gradient-free optimization scheme, with attractive mathematical properties, such as mean-field convergence results for non-convex loss functions. In this work, we study CBO in the context of closed-box adversarial attacks, whi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation#1131411
- Defending and mitigating the impact of targeted white-box adversarial attacks in computer network security#355765
- Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network#714011
- Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network#85092
- Adversarial Machine Learning in Cybersecurity: Attacks and Defenses#1179034
Record · ID 1012032
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive