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    This survey provides the first comprehensive review of backdoor attacks in deep neural networks (DNNs). It categorizes existing attacks and defenses, offering a framework for analysis and outlining future research directions in backdoor learning.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning Security

    Background:

    • Deep neural networks (DNNs) are vulnerable to backdoor attacks.
    • These attacks embed hidden triggers that manipulate model predictions.
    • Threats arise from untrusted training data or third-party models.

    Purpose of the Study:

    • To present the first comprehensive survey of backdoor attacks in DNNs.
    • To categorize existing backdoor attacks and defenses.
    • To provide a unified framework for analyzing poisoning-based backdoor attacks.

    Main Methods:

    • Systematic review and categorization of backdoor attacks and defenses.
    • Analysis of the relationship between backdoor attacks, adversarial attacks, and data poisoning.
    • Summary of widely adopted benchmark datasets for backdoor research.

    Main Results:

    • A structured overview of the current landscape of backdoor attacks and defenses.
    • A unified framework for understanding poisoning-based backdoor attacks.
    • Identification of key research areas and benchmark datasets.

    Conclusions:

    • Backdoor learning is a critical and growing research area requiring further investigation.
    • This survey serves as a foundational resource for researchers in the field.
    • Future research directions are identified to advance backdoor defense strategies.