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    Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

    Eungyu Lee, Yongsoo Lee, Taejin Lee*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1919-1935, 2023, DOI:10.32604/csse.2023.039599

    Abstract Artificial Intelligence (AI) technology has been extensively researched in various fields, including the field of malware detection. AI models must be trustworthy to introduce AI systems into critical decision-making and resource protection roles. The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI. Although various adversarial attack and defense methods are actively being studied, there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks. An AI model’s robustness level cannot be evaluated by traditional evaluation… More >

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