Calibrated Adversarial Sampling: Multi-Armed Bandit-Guided Generalization Against Unforeseen Attacks
PositiveArtificial Intelligence
- A new method called Calibrated Adversarial Sampling (CAS) has been introduced to enhance the robustness of Deep Neural Networks (DNNs) against unforeseen adversarial attacks, addressing the limitations of traditional adversarial training that often focuses on specific attack types. CAS employs a multi
- This development is significant as it provides a more comprehensive defense mechanism for DNNs, potentially reducing their vulnerability to a wider range of adversarial threats that may not have been considered during training.
- The ongoing research in adversarial training highlights the critical need for robust solutions in machine learning, as vulnerabilities in DNNs can lead to severe consequences in real
— via World Pulse Now AI Editorial System
