In the context of the rapidly evolving quantum era, where the capabilities of quantum computing are expanding exponentially, the security and reliability of generative AI anomaly detection systems have come under significant threat from adversarial attacks. This paper delves into the vulnerabilities inherent in existing models in this quantum era and introduces a novel approach aimed at fortifying their resilience. The problem at hand pertains to the susceptibility of conventional generative AI anomaly detection systems to adversarial manipulations, raising critical concerns about their trustworthiness in critical applications. Traditional methods in this domain rely on static models that lack adaptability to defend against quantum-based adversarial attacks, rendering them inadequate for the evolving threat landscape. Our proposed solution combines quantum-resistant algorithms with advanced generative AI techniques to dynamically adapt to emerging attack strategies, resulting in demonstrably enhanced robustness against quantum-based adversarial attacks, as substantiated by our experimental findings. In conclusion, safeguarding generative AI anomaly detection systems against adversarial threats in the quantum era is paramount, and our innovative approach offers a promising avenue for bolstering the security and reliability of these systems in this challenging environment.


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    Title :

    Adversarial Attacks on Generative AI Anomaly Detection in the Quantum Era


    Contributors:
    A, Jenefa (author) / Ebenezer, V (author) / Isaac, A Joshua (author) / Marshell, Joe (author) / Pradeepa, P. (author) / Naveen, V (author)


    Publication date :

    2023-11-22


    Size :

    422148 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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