  While text-to-image diffusion models demonstrate impressive generation
capabilities, they also exhibit vulnerability to backdoor attacks, which
involve the manipulation of model outputs through malicious triggers. In this
paper, for the first time, we propose a comprehensive defense method named
T2IShield to detect, localize, and mitigate such attacks. Specifically, we find
the "Assimilation Phenomenon" on the cross-attention maps caused by the
backdoor trigger. Based on this key insight, we propose two effective backdoor
detection methods: Frobenius Norm Threshold Truncation and Covariance
Discriminant Analysis. Besides, we introduce a binary-search approach to
localize the trigger within a backdoor sample and assess the efficacy of
existing concept editing methods in mitigating backdoor attacks. Empirical
evaluations on two advanced backdoor attack scenarios show the effectiveness of
our proposed defense method. For backdoor sample detection, T2IShield achieves
a detection F1 score of 88.9$\%$ with low computational cost. Furthermore,
T2IShield achieves a localization F1 score of 86.4$\%$ and invalidates 99$\%$
poisoned samples. Codes are released at https://github.com/Robin-WZQ/T2IShield.
