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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Deep learning advances image forgery localization but models are vulnerable to adversarial attacks.
    • Imperceptible noise in adversarial attacks significantly misleads current forgery localization models.

    Purpose of the Study:

    • To develop a defense mechanism against adversarial attacks for image forgery localization.
    • To enhance the robustness of deep learning models in detecting manipulated images.

    Main Methods:

    • Introduced an Adversarial Noise Suppression Module (ANSM) generating defensive perturbations.
    • Implemented a two-stage training strategy: Forgery-relevant Features Alignment (FFA) and Mask-guided Refinement (MgR).
    • FFA minimizes distributional discrepancies using Kullback-Leibler divergence; MgR uses dual-mask constraints for refinement.

    Main Results:

    • The proposed method significantly restores forgery localization performance on adversarial images.
    • Performance on original forged images remains largely unaffected when using the ANSM.
    • Extensive experiments confirm effectiveness across various attack algorithms.

    Conclusions:

    • The developed Adversarial Noise Suppression Module (ANSM) provides effective defense against adversarial attacks in image forgery localization.
    • This work represents the first reported adversarial defense specifically for image forgery localization tasks.
    • The source code and anti-forensics dataset are released for further research.