Defenses in Adversarial Machine Learning: A Systematic Survey from the Lifecycle Perspective

Abstract

This survey organizes defenses against adversarial examples, backdoor attacks, and weight attacks through the lifecycle of a machine-learning system. Its unified taxonomy spans pre-training, training, post-training, deployment, and inference, highlighting relationships among otherwise fragmented defense paradigms.

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