arXiv (All)
open access
Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
Test-time adaptation (TTA) mitigates distribution shifts by adapting models to unlabeled test inputs, but also exposes them to adversarial manipulation. Existing class-wise targeted attacks remain suboptimal for stealthy exploitation in this setting: since TTA operates on batches, forcing a subset o
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
- Sample-wise Targeted Adversarial Attacks on Test-time Adaptation#222548
- The Many Faces of Adversarial Machine Learning#199591
- Breaking Adversarial Transferability in Fine-Tuned Speech Recognition#1233148
- Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey of Availability Threats#1131933
- Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey of Availability Threats#1122215
Record · ID 1200944
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