Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating

Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating

Significant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions. However, such improvements in adversarial robu...

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Автори: Hossein Aboutalebi, Mohammad Javad Shafiee, Chi-En Amy Tai, Alexander Wong
Формат: Стаття
Мова:English
Опубліковано: IEEE 2024-01-01
Серія:IEEE Access
Предмети:
Adversarial attack
computer vision
deep learning
neural networks
Онлайн доступ:https://ieeexplore.ieee.org/document/10374121/

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https://ieeexplore.ieee.org/document/10374121/

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