Edge-Only Universal Adversarial Attacks in Distributed Learning

Summary

Researchers study whether universal adversarial perturbations (UAPs) can be generated when an attacker controls only the edge portion of a split model, meaning its initial network layers. They introduce edge-only untargeted and targeted UAP formulations that manipulate intermediate features before the split point. On ImageNet, the attacks transfer strongly to the unknown cloud component and compare favorably with classical white-box and black-box techniques.

Topics

• Adversarial machine learning

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