TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
arXiv preprint arXiv:2209.05244 , year=
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GLA backdoor attack on DriveVLM uses naturalistic graffiti and cross-lingual triggers to reach 90% ASR at 10% poisoning ratio while improving some clean-task metrics like BLEU-1.
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.
citing papers explorer
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TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
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Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers
GLA backdoor attack on DriveVLM uses naturalistic graffiti and cross-lingual triggers to reach 90% ASR at 10% poisoning ratio while improving some clean-task metrics like BLEU-1.
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A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models
A patch-augmented cross-view regularization method reduces backdoor attack success rates in multimodal LLMs by enforcing output differences between original and perturbed views while using entropy constraints to preserve benign generation quality.