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Related Experiment Video

Updated: Apr 9, 2026

A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
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S $^{4}$ ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Data-free Transferable Targeted

Yongxiang Liu, Bowen Peng, Li Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
    This summary is machine-generated.

    Transferable Targeted Attacks (TTAs) are improved by simple scaling transformations, offering a data-free method that enhances model security without needing victim model data.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning Security

    Background:

    • Transferable Targeted Attacks (TTAs) suffer from overfitting to surrogate models.
    • Existing data-free solutions often require black-box feedback, compromising fairness.
    • Current methods struggle with strict black-box transfer settings.

    Purpose of the Study:

    • To propose blind estimation measures for analyzing transformation effectiveness under black-box constraints.
    • To challenge conventional assumptions in TTA research.
    • To develop a data-free TTA method with state-of-the-art effectiveness and efficiency.

    Main Methods:

    • Introduced self-alignment and self-transferability measures for transformation analysis.
    • Investigated the impact of basic image transformations (scaling, geometric, color) on TTA.
    • Designed S⁴ST (Strong, Self-transferable, faSt, Simple Scale Transformation) integrating scaling, low-redundancy, and block-wise operations.

    Main Results:

    • Simple scaling transformations uniquely enhance targeted transferability.
    • Geometric and color transformations show high internal redundancy.
    • S⁴ST achieves state-of-the-art effectiveness-efficiency balance without data dependency across diverse settings.

    Conclusions:

    • Scaling's effectiveness is linked to the multi-scale nature of visual data and training augmentations.
    • The proposed blind estimation measures and S⁴ST framework offer a robust, data-free solution for TTAs.
    • The method demonstrates strong generalization capabilities on medical imaging and face verification tasks.