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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Cybersecurity

    Background:

    • No-Reference Image Quality Assessment (NR-IQA) models are crucial for image analysis.
    • Adversarial attacks reveal vulnerabilities but often lack transferability in black-box settings.
    • Existing white-box attacks fail against inaccessible NR-IQA models.

    Purpose of the Study:

    • To address the challenge of low transferability in black-box attacks against NR-IQA models.
    • To propose a novel transferable attack method for NR-IQA models.
    • To improve the robustness and security analysis of NR-IQA systems.

    Main Methods:

    • Proposed Signed Ensemble Gaussian black-box Attack (SEGA).
    • Approximated target model gradients using Gaussian smoothing and ensembling.
    • Implemented a perturbation filter mask for imperceptibility.

    Main Results:

    • SEGA demonstrated superior transferability compared to existing methods.
    • Successful black-box attacks were enabled against NR-IQA models.
    • The proposed method effectively reveals NR-IQA model vulnerabilities.

    Conclusions:

    • SEGA is an effective solution for transferable black-box attacks on NR-IQA models.
    • The findings guide the development of more robust NR-IQA systems.
    • This work advances adversarial attack research in image quality assessment.