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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Generalizable Black-Box Adversarial Attack With Meta Learning.

Fei Yin, Yong Zhang, Baoyuan Wu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 6, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a meta-learning framework to reduce query costs in black-box adversarial attacks by leveraging historical attack data. This approach enhances adversarial transferability for more efficient attacks.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Security

    Background:

    • Black-box adversarial attacks lack target model parameters, necessitating query-based feedback.
    • Existing methods incur high query costs due to limited feedback per example.
    • Adversarial transferability across examples and models is underexplored for efficiency.

    Purpose of the Study:

    • To develop a novel meta-learning framework to reduce query costs in black-box adversarial attacks.
    • To leverage both example-level and model-level adversarial transferability for enhanced attack efficiency.
    • To improve the performance of query-based black-box attack methods.

    Main Methods:

    • A meta-learning framework with a meta-generator trained to produce adversarial perturbations.
    • Utilizing example-level adversarial transferability by treating each attack as a meta-learning task.
    • Employing model-level adversarial transferability by pre-training on a white-box surrogate model.

    Main Results:

    • The proposed framework significantly reduces query costs for black-box adversarial attacks.
    • Meta-learning effectively fine-tunes the generator using historical attack feedback.
    • The approach boosts the performance of existing query-based attack methods.

    Conclusions:

    • The integration of example-level and model-level adversarial transferability offers a powerful strategy for efficient black-box attacks.
    • Meta-learning provides a robust mechanism for adapting to new attack tasks with minimal queries.
    • The framework demonstrates broad applicability and compatibility with various query-based attack techniques.