Adversarial Defense on Harmony: Reverse Attack for Robust AI Models Against Adversarial Attacks

Deep neural networks (DNNs) are crucial in safety-critical applications but vulnerable to adversarial attacks, where subtle perturbations cause misclassification. Existing defense mechanisms struggle with small perturbations and face accuracy-robustness trade-offs. This study introduces the “...

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Detalhes bibliográficos

Principais autores:	Yebon Kim, Jinhyo Jung, Hyunjun Kim, Hwisoo So, Yohan Ko, Aviral Shrivastava, Kyoungwoo Lee, Uiwon Hwang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2024-01-01
coleção:	IEEE Access
Assuntos:	Deep neural networks adversarial attacks and defenses security reliability
Acesso em linha:	https://ieeexplore.ieee.org/document/10766602/
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