A density-aware sample-specific backdoor attack steers triggers into low-density regions via bilevel optimization to achieve high post-defense success rates on image datasets.
Sampdetox: Black-box backdoor defense via perturbation-based sample detoxification.NeurIPS, 37:121236–121264, 2024b
3 Pith papers cite this work. Polarity classification is still indexing.
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TIGS detects backdoor-induced attention collapse in LLMs and applies content-aware tail-risk screening plus intrinsic geometric smoothing to suppress attacks while preserving normal performance.
TCAP detects backdoor samples in MLLM fine-tuning via tri-component attention profiling, GMM-based head identification, and EM vote aggregation.
citing papers explorer
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Density-aware Sample-specific Attack
A density-aware sample-specific backdoor attack steers triggers into low-density regions via bilevel optimization to achieve high post-defense success rates on image datasets.
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Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing
TIGS detects backdoor-induced attention collapse in LLMs and applies content-aware tail-risk screening plus intrinsic geometric smoothing to suppress attacks while preserving normal performance.
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TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning
TCAP detects backdoor samples in MLLM fine-tuning via tri-component attention profiling, GMM-based head identification, and EM vote aggregation.