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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multi-Label Adversarial Attack With New Measures and Self-Paced Constraint Weighting.

Fengguang Su, Ou Wu, Weiyao Zhu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 14, 2024
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    This study introduces refined measures for attack failure degree (AFD) and attack cost (AC) in multi-label learning. A novel self-paced weighting strategy improves adversarial attack gains while ensuring optimization stability.

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    Area of Science:

    • Machine Learning
    • Computer Vision

    Background:

    • Adversarial attacks in multi-label learning involve complex optimization problems.
    • Existing methods struggle with coarse measures for attack failure degree (AFD) and attack cost (AC), especially with conflicting constraints.

    Purpose of the Study:

    • To develop refined measures for AFD and AC in top-k adversarial attacks.
    • To formulate novel optimization problems addressing constraint violations.
    • To propose a self-paced weighting strategy for improved attack performance and stability.

    Main Methods:

    • Developed a Jaccard index-based measure for AFD and AC.
    • Formulated optimization problems minimizing constraint violation using new AFD/AC measures.
    • Implemented a self-paced weighting strategy for constraint prioritization.

    Main Results:

    • The Jaccard index measure better distinguishes attack failure degrees and costs.
    • Weighting slack variables theoretically improves optimization outcomes.
    • The self-paced weighting strategy yields larger attack gains and avoids optimization fluctuations.

    Conclusions:

    • The proposed refined measures and self-paced weighting strategy enhance adversarial attacks in multi-label learning.
    • The method demonstrates superior performance and stability across benchmark datasets.