Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models

Summary

This research introduces Dynamic Attention Analysis (DAA), a backdoor detection method for text-to-image diffusion models that tracks how cross-attention maps evolve over time rather than relying on static features. The authors report that backdoor samples show distinct evolution patterns at the $ $> token compared to benign samples. Across six backdoor attack scenarios, their DAA-S method achieved an average F1 Score of 79.27% and an AUC of 86.27%, surpassing existing detection methods.

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