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Updated: Jan 8, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

991

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

Zhongqi Wang, Jie Zhang, Shiguang Shan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 15, 2025
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces Dynamic Attention Analysis (DAA) to detect backdoor attacks in text-to-image diffusion models by analyzing dynamic feature evolution. DAA effectively identifies malicious samples by examining attention map anomalies, outperforming existing methods.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning Security

    Background:

    • Text-to-image diffusion models are susceptible to backdoor attacks using hidden textual triggers.
    • Existing backdoor detection methods often overlook the dynamic nature of diffusion models.

    Purpose of the Study:

    • To introduce a novel backdoor detection method, Dynamic Attention Analysis (DAA), leveraging the inherent dynamism of diffusion models.
    • To demonstrate that dynamic characteristics are superior indicators for detecting backdoor attacks compared to static features.

    Main Methods:

    • Examining the dynamic evolution of cross-attention maps to identify distinct feature patterns in backdoor samples at the token.
    • Introducing DAA-I, which quantifies dynamic anomalies using the Frobenius norm on spatially independent attention maps.
    • Proposing DAA-S, a dynamical system-based approach using graph-based state equations to model attention map interactions and analyze stability.

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    991

    Main Results:

    • Backdoor samples exhibit unique feature evolution patterns at the token compared to benign samples.
    • DAA-I and DAA-S effectively quantify dynamic anomalies in attention maps.
    • The proposed DAA approach significantly outperforms existing detection methods across six attack scenarios.

    Conclusions:

    • Dynamic Attention Analysis (DAA) offers a promising new perspective for detecting backdoor attacks in text-to-image diffusion models.
    • The dynamicity of diffusion models provides crucial indicators for robust backdoor detection.
    • DAA achieves high performance with an average F1 Score of 79.27% and AUC of 86.27%.