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    Machine learning (ML) algorithms face poisoning attacks. A new multiobjective bilevel optimization approach accounts for hyperparameter changes, providing a more realistic assessment of ML model robustness against these attacks.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Machine learning (ML) algorithms are susceptible to data poisoning attacks, where manipulated training data degrades performance.
    • Existing optimal attack strategies often assume fixed hyperparameters, leading to an overly pessimistic robustness evaluation.

    Purpose of the Study:

    • To develop a novel optimal attack formulation for ML poisoning attacks that considers hyperparameter adaptation.
    • To more accurately assess ML model robustness under worst-case attack scenarios.

    Main Methods:

    • Formulated optimal attacks as a multiobjective bilevel optimization problem, incorporating hyperparameter learning.
    • Applied the novel attack formulation to ML classifiers with L2 and L1 regularization.
    • Evaluated the approach on multiple datasets, including deep neural networks (DNNs).

    Main Results:

    • Constant regularization hyperparameter values can negatively impact algorithm performance.
    • The proposed method provides a more accurate assessment of robustness compared to previous approaches.
    • L2 and L1 regularization effectively mitigate poisoning attacks when hyperparameters are learned on trusted data.

    Conclusions:

    • Learning hyperparameters during attack formulation is crucial for realistic robustness evaluation.
    • Regularization is vital for enhancing the robustness and stability of complex models like DNNs against poisoning attacks.