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    Protecting deep image processing models from intellectual property theft is crucial. This study introduces a structure-aligned watermarking method robust against data augmentation attacks, enhancing model security.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep networks, particularly for image processing, are vulnerable to intellectual property theft via surrogate model attacks.
    • Existing watermarking techniques for classification tasks are ineffective against data augmentation attacks on image processing models.
    • Previous work on watermarking deep image processing networks relies on watermark consistency, which is disrupted by common augmentations.

    Purpose of the Study:

    • To develop a novel watermarking methodology for deep image processing networks that is robust against data augmentation attacks.
    • To enhance the protection of intellectual property for deep image processing models.
    • To improve the resilience of watermarking algorithms against sophisticated adversarial attacks.

    Main Methods:

    • Proposed a new watermarking methodology based on 'structure consistency'.
    • Designed a deep structure-aligned model watermarking algorithm embedding watermarks aligned with image structures like edges and semantic regions.
    • Evaluated the method's robustness against various data augmentation and adaptive attacks.

    Main Results:

    • The proposed structure-aligned watermarking method demonstrated superior robustness compared to baseline methods against data augmentation attacks.
    • Experiments confirmed the effectiveness of the watermarking technique in protecting deep image processing models.
    • The method showed good generalization ability and robustness against a wider range of adaptive attacks.

    Conclusions:

    • The structure consistency-based watermarking approach offers a robust solution for protecting intellectual property in deep image processing models.
    • This method effectively mitigates the vulnerabilities associated with data augmentation attacks in surrogate model training.
    • The developed algorithm provides enhanced security for deep image processing networks against various adversarial threats.