arXiv (All)
open access
Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse
Traditional backdoor attacks inject artificial trigger patterns into training data, causing models to misclassify triggered inputs while maintaining normal behavior on clean samples. Since these artificial triggers are absent in clean datasets, standard knowledge distillation (KD) typically eliminat
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
- Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse#1348784
- Models Designed to Forget: Machine Unlearning via Key Deletion#1128565
- Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery#973927
- DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss#1342856
- DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss#1318968
Record · ID 1356544
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