Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise enhanced interpretability. This study delves i...

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Autors principals:	Bader Rasheed, Mohamed Abdelhamid, Adil Khan, Igor Menezes, Asad Masood Khatak
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2024-01-01
Col·lecció:	IEEE Access
Matèries:	Concept bottleneck models adversarial attacks robustness interpretable models
Accés en línia:	https://ieeexplore.ieee.org/document/10677449/
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