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Physical Adversarial Attacks for Surveillance: A Survey.

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    Deep learning surveillance systems face physical adversarial attacks. This review analyzes these attacks on detection, identification, tracking, and action recognition, and discusses defenses to enhance system resilience.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Cybersecurity

    Background:

    • Deep learning powers modern automated surveillance but is vulnerable to adversarial attacks.
    • Physical adversarial attacks, like wearing disguised items, can trick surveillance systems, posing security risks.

    Purpose of the Study:

    • To review and analyze physical adversarial attacks in surveillance applications.
    • To propose a framework for analyzing these attacks and survey existing research.

    Main Methods:

    • A framework is proposed to analyze physical adversarial attacks.
    • A comprehensive survey of attacks on detection, identification, tracking, and action recognition is conducted.

    Main Results:

    • Physical adversarial attacks exploit vulnerabilities in deep learning surveillance systems.
    • Existing research on these attacks and defense strategies is analyzed.

    Conclusions:

    • Understanding and defending against physical adversarial attacks is crucial for enhancing surveillance system security and resilience.