<ul><li>Traditional deep neural networks face issues like catastrophic forgetting and vulnerability to adversarial attacks.</li><li>A new approach called SHIELD (Secure Hypernetworks for Incremental Expansion and Learning Defense) is introduced to address these challenges.</li><li>SHIELD integrates a hypernetwork-based continual learning approach with interval arithmetic to create separate networks for each subtask while aggregating information across all tasks.</li><li>The target models generated by SHIELD provide strict guarantees against possible attacks for data samples within defined interval ranges, enhancing security in continual learning.</li></ul>

SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense

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