SET detects input-level backdoors in T2I diffusion models by learning a benign cross-attention response space from clean samples and flagging deviations under multi-scale perturbations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
citing papers explorer
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Scaling Exposes the Trigger: Input-Level Backdoor Detection in Text-to-Image Diffusion Models via Cross-Attention Scaling
SET detects input-level backdoors in T2I diffusion models by learning a benign cross-attention response space from clean samples and flagging deviations under multi-scale perturbations.
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Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.