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Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However, in practical scenarios, task streams collected from untrusted sources may contain backdoor-poisoned samples, posing a critical challenge to the stability, plasticity, and security of continual learners. In this work, we investigate a challenging setting termed Continual Learning Under Backdoor Attack (CLUBA), where each incremental task may involve a small proportion of maliciously manipulated training samples. Unlike conventional continual lea

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