Research map / Adversarial robustness and security / Black-box and transfer attacks
Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks Using Hyperparameter Tuning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs against Query-Based Attacks (AAAI 2024 · similarity 0.89)
- Adapting to Evolving Adversaries with Regularized Continual Robust Training (ICML 2025 · similarity 0.88)
- On the Robustness of Distributed Machine Learning Against Transfer Attacks (AAAI 2025 · similarity 0.87)
- Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond (ICML 2025 · similarity 0.86)
- Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees (ICML 2025 · similarity 0.85)
- Enhancing Adversarial Transferability with Adversarial Weight Tuning (AAAI 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
